# kb

id: post-office-box-electricity-20260505-0
file: data/en.wikipedia.org/wiki/Post_office_box_(electricity)-0.md
category: reference
title: Post office box (electricity)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post_office_box_(electricity)
date_saved: 2026-05-05T03:41:55.080704+00:00
instance: kb-cron
parent: post-office-box-electricity-20260505-0
summary:
The post office box was a Wheatstone bridge–style testing device with pegs and spring arms to close electrical circuits and measure properties of the circuit under test. == Resistance measurement == The boxes were used in the United Kingdom by engineers from then General Post Office, who were responsible for UK telecommunications to trace electrical faults, i.e. to determine where a break occurred in a cable which could be several miles in length. It works on the principle of Wheatstone bridge to identify the resistance of wire connected and then by using wire resistivity and cross section calculating length of wire and thus determining where the cable had broken. Post office boxes were common pieces of scientific apparatus in the UK O-Level and A-Level schools public examination physics syllabus in the 1960s. == Construction == A typical post office box is in a wooden box with a hinged lid and a metal or bakelite panel showing circuit connections. Coils of wire are wound non-inductively, mounted in the body of the box, and have a negligible temperature coefficient. Pairs of ratio arms are each 5 10 20 ohms. Resistance arms contains a number of coils from 1 to 5000 ohms with


id: pqube-20260505-0
file: data/en.wikipedia.org/wiki/PQube-0.md
category: reference
title: PQube
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PQube
date_saved: 2026-05-05T03:41:56.285812+00:00
instance: kb-cron
parent: pqube-20260505-0
summary:
PQube is a registered trademark of Power Standards Lab for an electronic measuring instrument that records power quality and electric energy on the electric power grid. == Applications == PQube instruments are widely used to gather data for academic research, and at United States Department of Energy National Laboratories and state energy regulators. U.S. federal government agencies use PQubes to detect power quality issues – for example, the Federal Aviation Administration tracks disturbances at radar control centers. Each PQube instrument is traceable to the National Institute of Standards and Technology, so often these instruments are used in international academic and research environments. PQubes are a key element in many smart grid projects, recording power disturbance and power flow data to examine efficiency and reliability effects. == Information on the Web == Approximately 50 PQubes, located in approximately 40 countries, have been designated by their owners as free public sources of information at http://map.pqube.com . The site is updated approximately every 2 minutes with worldwide power quality and energy recordings. Data from these PQubes can be used, for example, for developing and testing power quality algorithms. Available data include daily, weekly, and monthly files in GIF and Microsoft Excel CSV format.


id: probe-tip-20260505-0
file: data/en.wikipedia.org/wiki/Probe_tip-0.md
category: reference
title: Probe tip
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probe_tip
date_saved: 2026-05-05T03:41:57.526681+00:00
instance: kb-cron
parent: probe-tip-20260505-0
summary:
A probe tip is an instrument used in scanning probe microscopes (SPMs) to scan the surface of a sample and make nano-scale images of surfaces and structures. The probe tip is mounted on the end of a cantilever and can be as sharp as a single atom. In microscopy, probe tip geometry (length, width, shape, aspect ratio, and tip apex radius) and the composition (material properties) of both the tip and the surface being probed directly affect resolution and imaging quality. Tip size and shape are extremely important in monitoring and detecting interactions between surfaces. SPMs can precisely measure electrostatic forces, magnetic forces, chemical bonding, Van der Waals forces, and capillary forces. SPMs can also reveal the morphology and topography of a surface. The use of probe-based tools began with the invention of scanning tunneling microscopy (STM) and atomic force microscopy (AFM), collectively called scanning probe microscopy (SPM) by Gerd Binnig and Heinrich Rohrer at the IBM Zurich research laboratory in 1982. It opened a new era for probing the nano-scale world of individual atoms and molecules as well as studying surface science, due to their unprecedented capability to characterize the mechanical, chemical, magnetic, and optical functionalities of various samples


id: probe-tip-20260505-1
file: data/en.wikipedia.org/wiki/Probe_tip-1.md
category: reference
title: Probe tip
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probe_tip
date_saved: 2026-05-05T03:41:57.526681+00:00
instance: kb-cron
parent: probe-tip-20260505-0
summary:
=== Non-conductive probe tip === Non-conductive nanoscale tips are widely used for AFM measurements. For non-conducting tip, surface forces acting on the tip/cantilever are responsible for deflection or attraction of tip. These attractive or repulsive forces are used for surface topology, chemical specifications, magnetic and electronic properties. The distance-dependent forces between substrate surface and tip are responsible for imaging in AFM. These interactions include van der Waals forces, capillary forces, electrostatic forces, Casimir forces, and solvation forces. One unique repulsion force is Pauli Exclusion repulsive force, which is responsible for single-atom imaging as in references and Figures 10 & 11 (contact region in Fig. 1). == Fabrication methods == Tip fabrication techniques fall into two broad classifications, mechanical and physicochemical. In the early stage of the development of probe tips, mechanical procedures were popular because of the ease of fabrication. === Mechanical methods === Reported mechanical methods in fabricating tips include cutting, grinding, and pulling.; an example would be cutting a wire at certain angles with a razor blade, wire cutter, or scissors. Another mechanical method for tip preparation is fragmentation of bulk pieces into small pointy pieces. Grinding a metal wire or rod into a sharp tip was also


id: probe-tip-20260505-2
file: data/en.wikipedia.org/wiki/Probe_tip-2.md
category: reference
title: Probe tip
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probe_tip
date_saved: 2026-05-05T03:41:57.526681+00:00
instance: kb-cron
parent: probe-tip-20260505-0
summary:
==== Lamella method ==== In the double lamella method, the lower part of the metal is etched away, and the upper part of the tip is not etched further. Further etching of the upper part of the wire is prevented by covering it with a polymer coating. This method is usually limited to laboratory fabrication. The double lamella method schematic is shown in Fig. 5. === Single atom tip preparation === Transitional metals like Cu, Au and Ag adsorb single molecules linearly on their surface due to weak van der Waals forces. This linear projection of single molecules allows interactions of the terminal atoms of the tip with the atoms of the substrate, resulting in Pauli repulsion for single molecule or atom mapping studies. Gaseous deposition on the tip is carried out in an ultrahigh vacuum (5 × 10−8 mbar) chamber at a low temperature (10K). Depositions of Xe, Kr, NO, CH4 or CO on tip have been successfully prepared and used for imaging studies. However, these tips preparations rely on the attachment of single atoms or molecules on the tip and the resulting atomic structure of the tip is not known exactly. The probability of attachment of simple molecules


id: probe-tip-20260505-3
file: data/en.wikipedia.org/wiki/Probe_tip-3.md
category: reference
title: Probe tip
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probe_tip
date_saved: 2026-05-05T03:41:57.526681+00:00
instance: kb-cron
parent: probe-tip-20260505-0
summary:
== Coating == The surface of silicon-based tips cannot be easily controlled because they usually carry the silanol group. The Si surface is hydrophilic and can be contaminated easily by the environment. Another disadvantage of Si tips is the wear and tear of the tip. It is important to coat the Si tip to prevent tip deterioration, and the tip coating may also enhance image quality. To coat a tip, an adhesive layer is pasted (usually chromium layer on 5 nm thick titanium) and then gold is deposited by vapor deposition (40-100 nm or less). Sometimes, the coating layer reduces the tunnelling current detection capability of probe tips. == Characterization == The most important aspect of a probe tip is imaging the surfaces efficiently at nanometre dimensions. Some concerns involving credibility of the imaging or measurement of the sample arise when the shape of the tip is not determined accurately. For example, when an unknown tip is used to measure a linewidth pattern or other high aspect ratio feature of a surface, there may remain some confusion when determining the contribution of the tip and of the sample in the acquired image. Consequently, it is important to fully and accurately


id: probe-tip-20260505-4
file: data/en.wikipedia.org/wiki/Probe_tip-4.md
category: reference
title: Probe tip
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probe_tip
date_saved: 2026-05-05T03:41:57.526681+00:00
instance: kb-cron
parent: probe-tip-20260505-0
summary:
Imaging tip using microscopy is used to take image of tip with microscopy, except scanning probe microscopy (SPM) e.g. scanning tunnelling microscopy (STM), atomic force microscopy (AFM) are reported. Using known tip characterizer is when the shape of tip is deduced by taking an image of a sample of known measurement, which is known as tip characterizer. Blind method is where tip characterizer of either known or unknown measurement is used. == Applications == Probes tips have a wide variety of applications in different fields of science and technology. One of the major areas where probe tips are used is for application in SPM i.e., STM and AFM. For example, carbon nanotube tips in conjunction with AFM provides an excellent tool for surface characterization in the nanometer realm. CNT tips are also used in tapping-mode Scanning Force Microscopy (SFM), which is a technique where a tip taps a surface by a cantilever driven near resonant frequency of the cantilever. The CNT probe tips fabricated using CVD technique can be used for imaging of biological macromolecules, semiconductor and chemical structure. For example, it is possible to obtain an intermittent AFM contact image of IgM macromolecules with excellent resolution using a single


id: rheoscope-20260505-0
file: data/en.wikipedia.org/wiki/Rheoscope-0.md
category: reference
title: Rheoscope
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rheoscope
date_saved: 2026-05-05T03:41:58.841941+00:00
instance: kb-cron
parent: rheoscope-20260505-0
summary:
In fluid mechanics (specifically rheology), a rheoscope is an instrument for detecting or measuring the viscosity of a fluid. In the study of blood flow, a rheoscope is used to observe and measure the deformation of blood cells subject to different levels of fluid shear stress. == References == Stein, Jess, ed. (1980). The Random House college dictionary. Random House. p. 1132. ISBN 0-394-43600-8.


id: scale-of-chords-20260505-0
file: data/en.wikipedia.org/wiki/Scale_of_chords-0.md
category: reference
title: Scale of chords
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scale_of_chords
date_saved: 2026-05-05T03:42:00.129566+00:00
instance: kb-cron
parent: scale-of-chords-20260505-0
summary:
A scale of chords may be used to set or read an angle in the absence of a protractor. To draw an angle, compasses describe an arc from origin with a radius taken from the 60 mark. The required angle is copied from the scale by the compasses, and an arc of this radius drawn from the sixty mark so it intersects the first arc. The line drawn from this point to the origin will be at the target angle. == Mathematics == A chord is a line drawn between two points on the circumference of a circle. Look at the centre point of this line. For a circle of radius r, each half will be r sin ⁡ θ 2 {\displaystyle r\sin {\tfrac {\theta }{2}}} so the chord will be 2 r sin ⁡ θ 2 {\displaystyle 2r\sin {\tfrac {\theta }{2}}} . The line of chords scale represents each of these values linearly on a scale running from 0 to 60. == Availability == It appears on Gunter's scale and the Foster Serle dialing scales. The commercial company Stanley marketed a metal version (Stanley 60R Line of Chords Rule) in 2015. == See also == Ptolemy's table of chords


id: scanning-habitable-environments-with-raman-and-luminescence-for-organics-and-chemicals-20260505-0
category: reference
title: Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals
tags: science, encyclopedia
date_saved: 2026-05-05T03:42:05.226992+00:00
instance: kb-cron
parent: scanning-habitable-environments-with-raman-and-luminescence-for-organics-and-chemicals-20260505-0
file:
data/en.wikipedia.org/wiki/Scanning_Habitable_Environments_with_Raman_and_Luminescence_for_Organics_and_Chemicals-0.md

summary:
Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is an ultraviolet Raman spectrometer that uses fine-scale imaging and an ultraviolet (UV) laser to determine fine-scale mineralogy, and detect organic compounds designed for the Perseverance rover as part of the Mars 2020 mission. It was constructed at the Jet Propulsion Laboratory with major subsystems being delivered from Malin Space Science Systems and Los Alamos National Laboratory. SHERLOC has a calibration target with possible Mars suit materials, and it will measure how they change over time in the Martian surface environment. == Goals == According to a 2017 Universities Space Research Association (USRA) report: The goals of the SHERLOC investigation are to: Assess the habitability potential of a sample and its aqueous history. Assess the availability of key elements and energy source for life (C, H, N, O, P, S etc.). Determine if there are potential biosignatures preserved in Martian rocks and outcrops. Provide organic and mineral analysis for selective sample caching. To do this SHERLOC does the following: Detects and classifies organics and astrobiologically relevant minerals on the surface and near subsurface of Mars. Bulk organic sensitivity of 10-5 to 10-6 w/w over a 7 x 7

source_url:
https://en.wikipedia.org/wiki/Scanning_Habitable_Environments_with_Raman_and_Luminescence_for_Organics_and_Chemicals


id: scintillating-bolometer-20260505-0
file: data/en.wikipedia.org/wiki/Scintillating_bolometer-0.md
category: reference
title: Scintillating bolometer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scintillating_bolometer
date_saved: 2026-05-05T03:42:06.405275+00:00
instance: kb-cron
parent: scintillating-bolometer-20260505-0
summary:
A scintillating bolometer (or luminescent bolometer) is a scientific instrument using particle physics in the search for events with low energy deposition. These events could include dark matter, low energy solar neutrinos, double beta decay or rare radioactive decay. It works by simultaneously measuring both the light pulse and heat pulse generated by a particle interaction within its internal scintillator crystal. The device was originally proposed by L. Gonzalez-Mestres and D. Perret-Gallix (LAPP, IN2P3/CNRS) In their rapporteur contribution to the Proceedings of the XXIV International Conference on High-Energy Physics, Munich, August 1988, Gonzalez-Mestres and Perret-Gallix wrote : Perhaps bolometry should in some cases be combined with other detection techniques (luminescence?) in order to produce a primary fast signal as timing strobe. If light is used as a complementary signature, particle identification can be achieved through the heat-light ratio, where nucleus recoil is expected to be less luminescent than ionizing particles. The success of such a development would open the way to unprecedented achievements in background rejection for rare event experiments. Further explanations, including a description of the detector and possible applications incorporating in particular BGO and tungstates, were given by these authors in other papers such as their contribution to


id: sensitive-high-resolution-ion-microprobe-20260505-0
file: data/en.wikipedia.org/wiki/Sensitive_high-resolution_ion_microprobe-0.md
category: reference
title: Sensitive high-resolution ion microprobe
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sensitive_high-resolution_ion_microprobe
date_saved: 2026-05-05T03:42:07.592588+00:00
instance: kb-cron
parent: sensitive-high-resolution-ion-microprobe-20260505-0
summary:
The sensitive high-resolution ion microprobe (also sensitive high mass-resolution ion microprobe or SHRIMP) is a large-diameter, double-focusing secondary ion mass spectrometer (SIMS) sector instrument that was produced by Australian Scientific Instruments in Canberra, Australia and now has been taken over by Chinese company Dunyi Technology Development Co. (DTDC) in Beijing. Similar to the IMS 1270-1280-1300 large-geometry ion microprobes produced by CAMECA, Gennevilliers, France and like other SIMS instruments, the SHRIMP microprobe bombards a sample under vacuum with a beam of primary ions that sputters secondary ions that are focused, filtered, and measured according to their energy and mass. The SHRIMP is primarily used for geological and geochemical applications. It can measure the isotopic and elemental abundances in minerals at a 10 to 30 μm-diameter scale and with a depth resolution of 1–5 μm. Thus, SIMS method is well-suited for the analysis of complex minerals, as often found in metamorphic terrains, some igneous rocks, and for relatively rapid analysis of statistical valid sets of detrital minerals from sedimentary rocks. The most common application of the instrument is in uranium–thorium–lead geochronology, although the SHRIMP can be used to measure some other isotope ratio measurements (e.g., δ7Li or δ11B) and trace element abundances.


id: sira-notified-body-20260505-0
file: data/en.wikipedia.org/wiki/Sira_(notified_body)-0.md
category: reference
title: Sira (notified body)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sira_(notified_body)
date_saved: 2026-05-05T03:42:08.827242+00:00
instance: kb-cron
parent: sira-notified-body-20260505-0
summary:
Sira is a UK-based notified body, specialising in ATEX, IECEX and North American product approvals. == Foundation == Sira began life as the British Scientific Instrument Research Association (BSIRA). It was founded in 1918 by a Committee of the Privy Council for the promotion of scientific and industrial research and supported by the DSIR. The first members of the association were representatives of the optical industry, but these were joined in the same year by the electrical scientific instrument, electromedical, and X-ray industries. Its first director of research was Sir Herbert Jackson (1863–1936). BSIRA's London headquarters were destroyed in the Second World War and, in 1947, the association moved to a site in South Hill, Chislehurst, a Grade II Listed former private house called 'Sitka' By the 1960s, the association had become better-known as 'Sira'. It evolved into a group of British engineering companies, based in south London, that designed test equipment and provided calibration services. == Fate == In 2006, Sira Test and Certification Ltd, Sira Defence and Security, and Sira Environmental were owned by Volveré plc. In July 2009, Volvere sold Sira Test and Certification Ltd, Sira Certification Service and Sira Environmental Ltd to CSA International. == Similar


id: spectronic-20-20260505-0
file: data/en.wikipedia.org/wiki/Spectronic_20-0.md
category: reference
title: Spectronic 20
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spectronic_20
date_saved: 2026-05-05T03:42:09.986392+00:00
instance: kb-cron
parent: spectronic-20-20260505-0
summary:
The Spectronic 20 is a brand of single-beam spectrophotometer, designed to operate in the visible spectrum across a wavelength range of 340 nm to 950 nm, with a spectral bandpass of 20 nm. It is designed for quantitative absorption measurement at single wavelengths. Because it measures the transmittance or absorption of visible light through a solution, it is sometimes referred to as a colorimeter. The name of the instrument is a trademark of the manufacturer. Developed by Bausch & Lomb and launched in 1953, the Spectronic 20 was the first low-cost spectrophotometer. It rapidly became an industry standard due to its low cost, durability and ease of use, and has been referred to as an "iconic lab spectrophotometer". Approximately 600,000 units were sold over its nearly 60 year production run. It has been the most widely used spectrophotometer worldwide. Production was discontinued in 2011 when it was replaced by the Spectronic 200, but the Spectronic 20 is still in common use. It is sometimes referred to as the "Spec 20". == Design == The Bausch & Lomb Spectronic 20 colorimeter uses a diffraction grating monochromator combined with a system for the detection, amplification, and measurement of light wavelengths in the


id: spectronic-20-20260505-1
file: data/en.wikipedia.org/wiki/Spectronic_20-1.md
category: reference
title: Spectronic 20
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spectronic_20
date_saved: 2026-05-05T03:42:09.986392+00:00
instance: kb-cron
parent: spectronic-20-20260505-0
summary:
== Use == Before testing a sample, the Spectronic 20 is calibrated using a blank solution, which is the pure solvent that is used in the experimental sample. It is typically water or an organic solvent. In this calibration, the transmittance is set at 100% using the calibration knob of the instrument (the amplifier control knob in the figure at right). The instrument can also optionally be calibrated with a stock solution of a sample at a concentration known to have an absorbance of 2 or else vendor supplied standards, using the light absorption knob in the diagram shown at right. After calibration, the user places a 1/2 inch test tube or cuvette containing the sample solution to be measured into the sample compartment. Calibration is repeated each time the wavelength is changed. It or a standard reference sample is generally used to periodically check for drift. To measure wavelengths above 650 nm, the bottom of the instrument is opened, and a red filter and a red-sensitive photocell is installed. The original design of the Spectronic 20 utilized an analog dial for readout of transmission from 100%T to 1%T (top scale), 0A - 2A (lower scale). Using the original instrument


id: total-ozone-mapping-spectrometer-20260505-0
file: data/en.wikipedia.org/wiki/Total_Ozone_Mapping_Spectrometer-0.md
category: reference
title: Total Ozone Mapping Spectrometer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Total_Ozone_Mapping_Spectrometer
date_saved: 2026-05-05T03:42:11.160247+00:00
instance: kb-cron
parent: total-ozone-mapping-spectrometer-20260505-0
summary:
The Total Ozone Mapping Spectrometer (TOMS) was a NASA satellite instrument, specifically a spectrometer, for measuring the ozone layer. Of the five TOMS instruments which were built, four entered successful orbit. The satellites carrying TOMS instruments were: Nimbus 7; launched October 24, 1978. Operated until 1 August 1994. Carried TOMS instrument number 1. Meteor-3-5; launched 15 August 1991. Operated until December 1994. Was the first and last Soviet satellite to carry a USA made instrument. Carried TOMS instrument number 2. ADEOS I; launched 17 August 1996. Operated until 30 June 1997. Mission was cut short by a spacecraft failure. TOMS-Earth Probe; launched on July 2, 1996. Operated until 2 December 2006. Carried TOMS instrument number 3. QuikTOMS; launched 21 September 2001. Suffered launch failure and did not enter orbit. Nimbus 7 and Meteor-3-5 provided global measurements of total column ozone on a daily basis and together provided a complete data set of daily ozone from November 1978 to December 1994. After an eighteen-month period when the program had no on-orbit capability, TOMS-Earth Probe launched on 2 July 1996, followed by ADEOS I. ADEOS I was launched on August 17, 1996, and the TOMS-instrument onboard provided data until the satellite which


id: xylotron-20260505-0
file: data/en.wikipedia.org/wiki/XyloTron-0.md
category: reference
title: XyloTron
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/XyloTron
date_saved: 2026-05-05T03:42:12.374459+00:00
instance: kb-cron
parent: xylotron-20260505-0
summary:
The XyloTron is an open-source, portable macroscopic wood identification system developed to support supply chain verification and the enforcement of laws against illegal logging. The device uses computer vision wood identification models to classify wood specimens based on their macroscopic anatomy and requires minimal operator training. Designed for use in laboratory and field settings, the system provides a non-destructive method to rapidly identify wood. The system's accuracy depends on the quality of the training data, the breadth and anatomical complexity of woods in the model, and the similarity of unknown samples to those included in the model. Use of the XyloTron is intended to help establish probable cause in cases of possible illegal logging, rather than for high-stakes legal enforcement or forensic confirmation. Microscopic analysis of wood specimens by a wood anatomist may still be required. == Overview == The XyloTron system was developed at the United States Department of Agriculture, Forest Service, Forest Products Laboratory in Madison, Wisconsin. Initial conceptual work began in late 2010. The XyloTron uses a laptop or desktop computer for computation and the XyloScope, which is specialized, custom-designed hardware that captures controlled imaging using a scientific-grade digital camera, lens, and lighting array. Unlike traditional wood


id: z-tube-20260505-0
file: data/en.wikipedia.org/wiki/Z-tube-0.md
category: reference
title: Z-tube
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Z-tube
date_saved: 2026-05-05T03:42:13.619607+00:00
instance: kb-cron
parent: z-tube-20260505-0
summary:
The Z-tube is an experimental apparatus for measuring the tensile strength of a liquid. It consists of a Z-shaped tube with open ends, filled with a liquid, and set on top of a spinning table. If the tube were straight, the liquid would immediately fly out one end or the other of the tube as it began to spin. By bending the ends of the tube back towards the center of rotation, a shift of the liquid away from center will result in the water level in one end of the tube rising and thus increasing the pressure in that end of the tube, and consequently returning the liquid to the center of the tube. By measuring the rotational speed and the distance from the center of rotation to the liquid level in the bent ends of the tube, the pressure reduction inside the tube can be calculated. Negative pressures, (i.e. less than zero absolute pressure, or in other words, tension) have been reported using water processed to remove dissolved gases. Tensile strengths up to 280 atmospheres have been reported for water in glass. == References ==


id: zeeman-slower-20260505-0
file: data/en.wikipedia.org/wiki/Zeeman_slower-0.md
category: reference
title: Zeeman slower
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Zeeman_slower
date_saved: 2026-05-05T03:42:14.782166+00:00
instance: kb-cron
parent: zeeman-slower-20260505-0
summary:
In atomic physics, a Zeeman slower is a scientific instrument that is commonly used in atomic physics to slow and cool a beam of hot atoms to speeds of several meters per second and temperatures below a kelvin. The gas-phase atoms used in atomic physics are often generated in an oven by heating a solid or liquid atomic sample to temperatures where the vapor pressure is high enough that a substantial number of atoms are in the gas phase. These atoms effuse out of a hole in the oven with average speeds on the order of hundreds of m/s and large velocity distributions (due to their high temperature). The Zeeman slower is attached close to where the hot atoms exit the oven and are used to slow them to less than 10 m/s (slowing) with a very small velocity spread (cooling). A Zeeman slower consists of a cylinder, through which an atomic beam travels, a pump laser that counterpropagates with respect to the beam's direction, and a magnetic field (commonly produced by a solenoid-like coil) that points along the cylinder's axis with a spatially varying magnitude. The pump laser, which is required to be near-resonant with atomic transition, Doppler-slows a


id: zeeman-slower-20260505-1
file: data/en.wikipedia.org/wiki/Zeeman_slower-1.md
category: reference
title: Zeeman slower
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Zeeman_slower
date_saved: 2026-05-05T03:42:14.782166+00:00
instance: kb-cron
parent: zeeman-slower-20260505-0
summary:
B ( z ) = ℏ k μ ′ v + ℏ δ μ ′ = ℏ k v i μ ′ 1 − 2 a v i 2 z + ℏ δ μ ′ , {\displaystyle B(z)={\frac {\hbar k}{\mu '}}v+{\frac {\hbar \delta }{\mu '}}={\frac {\hbar kv_{i}}{\mu '}}{\sqrt {1-{\frac {2a}{v_{i}^{2}}}z}}+{\frac {\hbar \delta }{\mu '}},} where v i {\displaystyle v_{i}} is the maximal velocity class that will be slowed; all the atoms in the velocity distribution that have velocities v < v i {\displaystyle v<v_{i}} will be slowed, and those with velocities v > v i {\displaystyle v>v_{i}} will not be slowed at all. The parameter η {\displaystyle \eta } (which determines the required laser intensity) is normally chosen to be around 0.5. If a Zeeman slower were to be operated with η ≈ 1 {\displaystyle \eta \approx 1} , then after absorbing a photon and moving to the excited state, the atom would preferentially re-emit a photon in the direction of the laser beam (due to stimulated emission), which would counteract the slowing process. == Realization == The required form of the spatially inhomogeneous magnetic field as we showed above has the form B ( z ) = B 0


id: metascience-20260505-0
file: data/en.wikipedia.org/wiki/Metascience-0.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience (also known as meta-research) is the use of scientific methodology to study science itself. Metascience seeks to increase the quality of scientific research and enhance its efficiency. It is also known as "research on research" and "the science of science", as it uses research methods to study how research is done and find where improvements can be made. Metascience concerns itself with all fields of research and has been described as "a bird's eye view of science". In the words of John Ioannidis, "Science is the best thing that has happened to human beings ... but we can do it better." In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. It found that "in almost 73% of the reports read ... conclusions were drawn when the justification for these conclusions was invalid." Meta-research in the following decades found many methodological flaws, inefficiencies, and poor practices in research across numerous scientific fields. Many scientific studies could not be reproduced, particularly in medicine and the soft sciences. The term "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Measures have been implemented to


id: metascience-20260505-1
file: data/en.wikipedia.org/wiki/Metascience-1.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
=== Reporting === Meta-research has identified poor practices in reporting, explaining, disseminating and popularizing research, particularly within the social and health sciences. Poor reporting makes it difficult to accurately interpret the results of scientific studies, to replicate studies, and to identify biases and conflicts of interest in the authors. Solutions include the implementation of reporting standards, and greater transparency in scientific studies (including better requirements for disclosure of conflicts of interest). There is an attempt to standardize reporting of data and methodology through the creation of guidelines by reporting agencies such as CONSORT and the larger EQUATOR Network. === Reproducibility === The replication crisis is an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate. While the crisis has its roots in the meta-research of the mid- to late 20th century, the phrase "replication crisis" was not coined until the early 2010s as part of a growing awareness of the problem. The replication crisis has been closely studied in psychology (especially social psychology) and medicine, including cancer research. Replication is an essential part of the scientific process, and the widespread failure of replication puts into question the reliability of affected


id: metascience-20260505-2
file: data/en.wikipedia.org/wiki/Metascience-2.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
===== Science funding and awards ===== Scientific awards are one category of science incentives. Metascience can explore existing and hypothetical systems of science awards. For instance, it found that work honored by Nobel Prizes clustered in only a few scientific fields with only 36/71 having received at least one Nobel Prize. Of the 114/849 domains science could be divided into their DC2 and DC3 classification systems, five were shown to comprise over half of the Nobel Prizes awarded between 1995 and 2017 (particle physics [14%], cell biology [12.1%], atomic physics [10.9%], neuroscience [10.1%], molecular chemistry [5.3%]). A study found that delegation of responsibility by policy-makers – a centralized authority-based top-down approach – for knowledge production and appropriate funding to science with science subsequently somehow delivering "reliable and useful knowledge to society" is too simple. Measurements show that allocation of bio-medical resources can be more strongly correlated to previous allocations and research than to burden of diseases. A study suggests that "[i]f peer review is maintained as the primary mechanism of arbitration in the competitive selection of research reports and funding, then the scientific community needs to make sure it is not arbitrary". Studies indicate there to is a need to


id: metascience-20260505-3
file: data/en.wikipedia.org/wiki/Metascience-3.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience can investigate how scientific processes evolve over time. A study found that teams are growing in size, "increasing by an average of 17% per decade". (see labor advantage below) It was found that prevalent forms of non-open access publication and prices charged for many conventional journals – even for publicly funded papers – are unwarranted, unnecessary – or suboptimal – and detrimental barriers to scientific progress. Open access can save considerable amounts of financial resources, which could be used otherwise, and level the playing field for researchers in developing countries. There are substantial expenses for subscriptions, gaining access to specific studies, and for article processing charges. Paywall: The Business of Scholarship is a documentary on such issues. Another topic are the established styles of scientific communication (e.g. long text-form studies and reviews) and the scientific publishing practices – there are concerns about a "glacial pace" of conventional publishing. The use of preprint-servers to publish study-drafts early is increasing and open peer review, new tools to screen studies, and improved matching of submitted manuscripts to reviewers are among the proposals to speed up publication. ==== Science overall and intrafield developments ==== Metadata from research publications can be extracted, enriched, and


id: metascience-20260505-4
file: data/en.wikipedia.org/wiki/Metascience-4.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Some research is investigating scientific controversy or controversies, and may identify currently ongoing major debates (e.g. open questions), and disagreement between scientists or studies. One study suggests the level of disagreement was highest in the social sciences and humanities (0.61%), followed by biomedical and health sciences (0.41%), life and earth sciences (0.29%); physical sciences and engineering (0.15%), and mathematics and computer science (0.06%). Such research may also show, where the disagreements are, especially if they cluster, including visually such as with cluster diagrams. === Challenges of interpretation of pooled results === Studies about a specific research question or research topic are often reviewed in the form of higher-level overviews in which results from various studies are integrated, compared, critically analyzed and interpreted. Examples of such works are scientific reviews and meta-analyses. These and related practices face various challenges and are a subject of metascience. Various issues with available studies such as, for example, heterogeneity of methods used may lead to faulty conclusions of the meta-analysis. === Knowledge integration and living documents === Various problems require swift integration of new and existing science-based knowledge. Especially setting where there are a large number of loosely related projects and initiatives benefit from a


id: metascience-20260505-5
file: data/en.wikipedia.org/wiki/Metascience-5.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
==== As and for acceleration of progress ==== A study suggests that improving the way science is done could accelerate the rate of scientific discovery and its applications which could be useful for finding urgent solutions to humanity's problems, improve humanity's conditions, and enhance understanding of nature. Metascientific studies can seek to identify aspects of science that need improvement, and develop ways to improve them. If science is accepted as the fundamental engine of economic growth and social progress, this could raise "the question of what we – as a society – can do to accelerate science, and to direct science toward solving society's most important problems." One author clarified that funding science requires diverse approaches rather than a single model. DARPA models, curiosity-driven research, peer-champion mechanisms, and other approaches each have merit and application in different contexts. Nevertheless, evaluation of them can help build knowledge of what works or works best. == Reforms == Meta-research identifying flaws in scientific practice has inspired reforms in science. These reforms seek to address and fix problems in scientific practice which lead to low-quality or inefficient research. A 2015 study lists "fragmented" efforts in meta-research. === Pre-registration === The practice of registering a


id: metascience-20260505-6
file: data/en.wikipedia.org/wiki/Metascience-6.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:42:16.492073+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Clinical research in medicine is often of low quality, and many studies cannot be replicated. An estimated 85% of research funding is wasted. Additionally, the presence of bias affects research quality. The pharmaceutical industry exerts substantial influence on the design and execution of medical research. Conflicts of interest are common among authors of medical literature and among editors of medical journals. While almost all medical journals require their authors to disclose conflicts of interest, editors are not required to do so. Financial conflicts of interest have been linked to higher rates of positive study results. In antidepressant trials, pharmaceutical sponsorship is the best predictor of trial outcome. Blinding is another focus of meta-research, as error caused by poor blinding is a source of experimental bias. Blinding is not well reported in medical literature, and widespread misunderstanding of the subject has resulted in poor implementation of blinding in clinical trials. Furthermore, failure of blinding is rarely measured or reported. Research showing the failure of blinding in antidepressant trials has led some scientists to argue that antidepressants are no better than placebo. In light of meta-research showing failures of blinding, CONSORT standards recommend that all clinical trials assess and report the quality


id: alltrials-20260505-0
file: data/en.wikipedia.org/wiki/AllTrials-0.md
category: reference
title: AllTrials
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AllTrials
date_saved: 2026-05-05T03:42:21.460760+00:00
instance: kb-cron
parent: alltrials-20260505-0
summary:
AllTrials (sometimes called All Trials or AllTrials.net) is a project advocating that clinical research adopt the principles of open research. The project summarizes itself as "All trials registered, all results reported": that is, all clinical trials should be listed in a clinical trials registry, and their results should always be shared as open data. At the center of the organisation is a petition signed by over 85,000 individuals and 599 organisations (as of August 2015): Thousands of clinical trials have not reported their results; some have not even been registered. Information on what was done and what was found in these trials could be lost forever to doctors and researchers, leading to bad treatment decisions, missed opportunities for good medicine, and trials being repeated. All trials past and present should be registered, and the full methods and the results reported. We call on governments, regulators and research bodies to implement measures to achieve this. Ben Goldacre, author of Bad Science and Bad Pharma, is a founder of the campaign and its most public spokesperson. In 2016, he participated in the launch of the OpenTrials database. AllTrials is an international initiative of Bad Science, BMJ, Centre for Evidence-based Medicine, Cochrane Collaboration,


id: alltrials-20260505-1
file: data/en.wikipedia.org/wiki/AllTrials-1.md
category: reference
title: AllTrials
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AllTrials
date_saved: 2026-05-05T03:42:21.460760+00:00
instance: kb-cron
parent: alltrials-20260505-0
summary:
== Supporters == The campaign is an initiative of Sense about Science, Centre for Evidence Based Medicine, The Dartmouth Institute for Health Policy and Clinical Practice, James Lind Alliance, Cochrane Collaboration, BMJ Group, PLOS, and Bad Science. The petition statement of AllTrials has been signed by organizations including Wellcome Trust, British Library, Medical Research Council (UK), British Heart Foundation, Institute for Quality and Efficiency in Health Care, National Institute for Health and Care Excellence, BioMed Central, National Physicians Alliance, Royal Society of Medicine, Health Research Authority, American Medical Student Association, GlaxoSmithKline, and others. As of May 2017, The AllTrials petition has been signed by 90,282 people and 721 organisations. In October 2016, AllTrials published a road map detailing steps that various types of organisations can take to get more trials registered and more results reported. 85 investors with >€3.5 trillion (£2.45trn; $3.83trn) of investments have supported AllTrials (as of July 2015), with Peter van der Werf of RobecoSAM saying: "We deem this to be a financially material factor and encourage all companies to gain credibility regarding their approach to clinical trial transparency by signing up to the AllTrials principles.". The Laura and John Arnold Foundation provided early and ongoing financial


id: c-glenn-begley-20260505-0
file: data/en.wikipedia.org/wiki/C._Glenn_Begley-0.md
category: reference
title: C. Glenn Begley
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/C._Glenn_Begley
date_saved: 2026-05-05T03:42:22.624290+00:00
instance: kb-cron
parent: c-glenn-begley-20260505-0
summary:
C. Glenn Begley is a hematologist and oncologist who was the CEO of BioCurate, an Australia-based joint venture between the University of Melbourne and Monash University that was launched in 2016. The "C. Glenn Begley Award", created by Stanford University in 2025, was named in his honor. The award celebrates teams that uphold the highest standards of scientific integrity, fostering innovation that is both impactful and reliable. Previously, he worked at the California-based biotech company Akriveia Therapeutics as their chief scientific officer. His other previous positions include global head of hematology and oncology research at Amgen, senior vice president and chief scientific officer at TetraLogic Pharmaceuticals, and executive director of the Western Australian Institute of Medical Research. He studied medicine at the University of Melbourne. He is known for a study he co-authored in 2012 in which he found that only 6 out of 53 (11%) of landmark preclinical studies of cancer drugs could be reproduced. He was elected to the American Society for Clinical Investigation in 2000. Begley was elected Fellow of the Australian Academy of Health and Medical Sciences in 2015. == Education == Begley studied medicine at the University of Melbourne starting in 1972 and finishing in


id: berkeley-initiative-for-transparency-in-the-social-sciences-20260505-0
file: data/en.wikipedia.org/wiki/Berkeley_Initiative_for_Transparency_in_the_Social_Sciences-0.md
category: reference
title: Berkeley Initiative for Transparency in the Social Sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Berkeley_Initiative_for_Transparency_in_the_Social_Sciences
date_saved: 2026-05-05T03:42:23.767060+00:00
instance: kb-cron
parent: berkeley-initiative-for-transparency-in-the-social-sciences-20260505-0
summary:
The Berkeley Initiative for Transparency in the Social Sciences, abbreviated BITSS, is an academic initiative dedicated to advancing transparency, reproducibility, and openness in social science research. It was established in 2012 by the University of California, Berkeley's Center for Effective Global Action. It has worked with the Center for Open Science to define and promote a set of best practices for social scientists to maximize transparency in their research. BITSS has also worked to promote registered reports, supporting journals like the Journal of Development Economics in taking up the review track. In 2015, BITSS began awarding the annual Leamer-Rosenthal Prizes for Open Social Science to honor outstanding achievements and emerging leaders in promoting transparency in social science. Through its Catalyst program, the initiative also supports and empowers over 150 graduate students, faculty, librarians, and early career researchers to advance open science all over the world. Their annual Research Transparency and Reproducibility Training (RT2) provides an overview of and hands-on practice with tools and practices for transparent and reproducible social science research. Their Massive Open Online Course "Transparent and Open Social Science Research,” based on a UC Berkeley course taught by Edward Miguel, is available on the FutureLearn platform. In 2019,


id: burden-of-knowledge-20260505-0
file: data/en.wikipedia.org/wiki/Burden_of_knowledge-0.md
category: reference
title: Burden of knowledge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Burden_of_knowledge
date_saved: 2026-05-05T03:42:24.900126+00:00
instance: kb-cron
parent: burden-of-knowledge-20260505-0
summary:
The burden of knowledge describes the difficulty of adding to a scientific field as the amount of previous work that must be understood increases over time. This is seen across scientific disciplines. Evidence for this burden includes a trend that people are older before they receive their first patent or publish in a prestigious journal, and the need for scientific groups to be larger as scientists need to collaborate so that the team has sufficient understanding of prior work. == Scholarship == Explicit scholarship of this idea has entered the mainstream with the works of Benjamin Jones, in particular The Burden of Knowledge and the Death of the Renaissance Man, and with the works of Jan Brendel and Sascha Schweitzer The Burden of Knowledge in Mathematics. == Overview == Theory and empirical studies reflect the case that researchers and innovators are not born with the required expertise and must first undertake education. With accumulating information and discoveries, the time to digest and improve on extant knowledge takes longer. Similarly, frontiers of knowledge advance at an overall increasing rate and are shifting over time. The "burden of knowledge" refers to the difficulty of catching up with this evolving knowledge frontier. A


id: conflicts-of-interest-in-academic-publishing-20260505-0
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-0.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:42:26.093387+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
Conflicts of interest (COIs) often arise in academic publishing. Such conflicts may cause wrongdoing and make it more likely. Ethical standards in academic publishing exist to avoid and deal with conflicts of interest, and the field continues to develop new standards. Standards vary between journals and are unevenly applied. According to the International Committee of Medical Journal Editors, "[a]uthors have a responsibility to evaluate the integrity, history, practices and reputation of the journals to which they submit manuscripts". Conflicts of interest increase the likelihood of biases arising; they can harm the quality of research and the public good (even if disclosed). Conflicts of interest can involve research sponsors, authors, journals, journal staff, publishers, and peer reviewers. == Avoidance, disclosure, and tracking == The avoidance of conflicts of interest and the changing of the structure of institutions to make them easier to avoid are frequently advocated for. Some institutional ethics policies ban academics from entering into specific types of COIs, for instance by prohibiting them from accepting gifts from companies connected with their work. Education in ethical COI management is also a tool for avoiding COI problems. Disclosure of COIs has been debated since the 1980s; there is a general consensus


id: conflicts-of-interest-in-academic-publishing-20260505-1
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-1.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:42:26.093387+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
==== Owners and governing bodies ==== The owner of an academic journal has ultimate power over the hiring and firing of editorial staff; editors' interests in pleasing their employers conflict with some of their other editorial interests. Journals are also more likely to accept papers by authors who work for the journals' hosting institutions. Some journals are owned by publishers. When journals print reviews of books published by their own publishers, they rarely (as of 2013) add COI disclosures. The publishers' interest in maximizing profit will often conflict with academic interests or ethical standards. In the case of closed-access publications, publishers' desire for high subscription income may conflict with an editorial desire for broader access and readership. There have been multiple mass resignations of editorial boards over such conflicts, which are often followed by the editorial board founding a new, non-profit journal to compete with their former one. Some journals are owned by academic societies and professional organisations. Leading journals can be very profitable and there is often friction about revenue between the journal and the member society that owns it. Some academic societies and professional organisations are themselves funded by membership fees and/or donations. If the owners benefit financially


id: conflicts-of-interest-in-academic-publishing-20260505-2
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-2.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:42:26.093387+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
=== COIs of journal staff === Personal conflicts of interest faced by journal staff are individual. If a person leaves the journal—unlike the COIs of journals as institutions—their personal COIs will go with them. As of 2015, COIs of journal staff are less commonly reported than those of authors. For instance, one 2009 World Association of Medical Editors (WAME) policy document states, "Some journals list editors' competing interests on their website but this is not a standard practice". The ICMJE, however, requires that the COIs of editors and journal staff be regularly declared and published. One 2017 Open Payments study of influential US medical journals found half of the editors received payments from industry; another study that used a different sample of editors reported two-thirds. As of 2002, systems for reporting wrongdoing by editors often do not exist. Many journals have policies limiting COIs staff can enter into; for instance, accepting gifts of travel, accommodation, or hospitality may be prohibited. As of 2016, such policies are rarely published. Most journals do not offer COI training; as of 2015, many journals report a desire for better guidance on COI policy. === COIs of peer reviewers === The ICJME recommendations require peer


id: conflicts-of-interest-in-academic-publishing-20260505-3
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-3.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T03:42:26.093387+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
The ICMJE requires that "All those designated as authors should meet all four criteria for authorship, and all who meet the four criteria should be identified as authors. Those who do not meet all four criteria should be acknowledged." Academics who have had publication ethics training and those who are aware of the ICMJE authorship criteria are more stringent in their concepts of authorship and are more likely to consider breaches of authorship as misconduct, as are more junior researchers. Awareness is low; one study found only about half of researchers had read the ICJME criteria. === COIs of study sponsors === If a study requires outside funding, this can be a major source of conflicting interests; for instance in cases where the manufacturer of a drug is funding a study into its safety and efficacy or where the sponsor hopes to use the research to defend itself in litigation. Sponsors of a study may involve themselves in the design, execution, analysis, and write-up of a study. In extreme cases, they may carry out the research and ghostwrite the article with almost no involvement from the nominal author. Movie-style credits are advocated as a way to avoid this. There are


id: design-of-experiments-20260505-0
file: data/en.wikipedia.org/wiki/Design_of_experiments-0.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:42:27.446946+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
The design of experiments (DOE), also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation. In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as "input variables" or "predictor variables." The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as "output variables" or "response variables." The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results. Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. There are multiple approaches


id: design-of-experiments-20260505-1
file: data/en.wikipedia.org/wiki/Design_of_experiments-1.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:42:27.446946+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
Comparison In some fields of study it is not possible to have independent measurements to a traceable metrology standard. Comparisons between treatments are much more valuable and are usually preferable, and often compared against a scientific control or traditional treatment that acts as baseline. Randomization Random assignment is the process of assigning individuals at random to groups or to different groups in an experiment, so that each individual of the population has the same chance of becoming a participant in the study. The random assignment of individuals to groups (or conditions within a group) distinguishes a rigorous, "true" experiment from an observational study or "quasi-experiment". There is an extensive body of mathematical theory that explores the consequences of making the allocation of units to treatments by means of some random mechanism (such as tables of random numbers, or the use of randomization devices such as playing cards or dice). Assigning units to treatments at random tends to mitigate confounding, which makes effects due to factors other than the treatment to appear to result from the treatment. The risks associated with random allocation (such as having a serious imbalance in a key characteristic between a treatment group and a control group)


id: design-of-experiments-20260505-2
file: data/en.wikipedia.org/wiki/Design_of_experiments-2.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:42:27.446946+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
Let yi be the measured difference for i = 1, ..., 8. The relationship between the true weights and experimental measurements may be represented with a general linear model, with the design matrix W {\displaystyle W} having entries from { − 1 , 0 , 1 } {\displaystyle \{-1,0,1\}} : y = W θ + ϵ {\displaystyle y=W\theta +\epsilon } The first design is represented by an identity matrix while the second design is represented by an 8x8 Hadamard matrix, H {\displaystyle H} , both examples of weighing matrices. The weights are typically estimated using the method of least squares. Using a weighing matrix, this is equivalent to inverting on the measurements: θ ^ A = I − 1 y = y {\displaystyle {\hat {\theta }}_{A}=I^{-1}y=y} θ ^ B = H − 1 y {\displaystyle {\hat {\theta }}_{B}=H^{-1}y} The question of design of experiments is: which experiment is better? Investigating estimate A vs B for the first weight: Var ⁡ ( θ ^ A , 1 ) = Var ⁡ ( y 1 ) = σ 2 {\displaystyle \operatorname {Var} ({\hat {\theta }}_{A,1})=\operatorname {Var} (y_{1})=\sigma ^{2}} Var ⁡ ( θ ^ B , 1 ) = Var ⁡ ( y


id: design-of-experiments-20260505-3
file: data/en.wikipedia.org/wiki/Design_of_experiments-3.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:42:27.446946+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
== Causal attributions == In the pure experimental design, the independent (predictor) variable is manipulated by the researcher – that is – every participant of the research is chosen randomly from the population, and each participant chosen is assigned randomly to conditions of the independent variable. Only when this is done is it possible to certify with high probability that the reason for the differences in the outcome variables are caused by the different conditions. Therefore, researchers should choose the experimental design over other design types whenever possible. However, the nature of the independent variable does not always allow for manipulation. In those cases, researchers must be aware of not certifying about causal attribution when their design doesn't allow for it. For example, in observational designs, participants are not assigned randomly to conditions, and so if there are differences found in outcome variables between conditions, it is likely that there is something other than the differences between the conditions that causes the differences in outcomes, that is – a third variable. The same goes for studies with correlational design. == Statistical control == It is best that a process be in reasonable statistical control prior to conducting designed experiments. When


id: design-of-experiments-20260505-4
file: data/en.wikipedia.org/wiki/Design_of_experiments-4.md
category: reference
title: Design of experiments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Design_of_experiments
date_saved: 2026-05-05T03:42:27.446946+00:00
instance: kb-cron
parent: design-of-experiments-20260505-0
summary:
== See also == Adversarial collaboration – Method of research Bayesian experimental design – Experimental design framework Block design – Structure in combinatorial mathematics Box–Behnken design – Experimental designs for response surface methodology Central composite design – Experimental design in statistical mathematics Clinical study design – Plan for research in clinical medicine Computer experiment – Experiment used to study computer simulation Controlling for a variable – Binning data according to measured values of the variable Experimetrics (econometrics-related experiments) Factor analysis – Statistical method Fractional factorial design – Statistical experimental design approach Glossary of experimental design Grey box model – Mathematical data production model with limited structure Industrial engineering – Branch of engineering which deals with the optimization of complex processes or systems Instrument effect Law of large numbers – Averages of repeated trials converge to the expected value Manipulation checks Multifactor design of experiments software One-factor-at-a-time method – Method of designing experiments Optimal design – Experimental design that is optimal with respect to some statistical criterionPages displaying short descriptions of redirect targets Plackett–Burman design – Type of experimental design Probabilistic design – Discipline within engineering design Protocol (natural sciences) – Procedural method for the design and implementation of an experimentPages


id: further-research-is-needed-20260505-0
file: data/en.wikipedia.org/wiki/Further_research_is_needed-0.md
category: reference
title: Further research is needed
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Further_research_is_needed
date_saved: 2026-05-05T03:42:28.658732+00:00
instance: kb-cron
parent: further-research-is-needed-20260505-0
summary:
"Further research is needed" (FRIN), "more research is needed" and other variants of similar phrases are commonly used in research papers. The cliché is so common that it has attracted research, regulation and cultural commentary. == Meaning == Some research journals have banned the phrase "more research is needed" on the grounds that it is redundant; it is almost always true and fits almost any article, and so can be taken as understood. A 2004 metareview by the Cochrane collaboration of their own systematic medical reviews found that 93% of the reviews studied made indiscriminate FRIN-like statements, reducing their ability to guide future research. The presence of FRIN had no correlation with the strength of the evidence against the medical intervention. Authors who thought a treatment was useless were just as likely to recommend researching it further. Indeed, authors may recommend "further research" when, given the existing evidence, further research would be extremely unlikely to be approved by an ethics committee. Studies finding that a treatment has no noticeable effects are sometimes greeted with statements that "more research is needed" by those convinced that the treatment is effective, but the effect has not yet been found. Since even the largest


id: steven-n-goodman-20260505-0
file: data/en.wikipedia.org/wiki/Steven_N._Goodman-0.md
category: reference
title: Steven N. Goodman
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steven_N._Goodman
date_saved: 2026-05-05T03:42:29.822988+00:00
instance: kb-cron
parent: steven-n-goodman-20260505-0
summary:
Steven N. Goodman (born 1954) is an American epidemiologist and Professor of Epidemiology and Population Health and of Medicine at the Stanford School of Medicine, where he serves as Associate Dean of Clinical and Translational Research. He is co-founder and co-director of the Meta-Research Innovation Center at Stanford (METRICS), a center dedicated to studying and improving the reproducibility and efficiency of biomedical research, alongside John Ioannidis. He is also founder and director of the Stanford Program on Research Rigor and Reproducibility (SPORR). Goodman has made extensive contributions to the foundations of scientific and statistical inference within the biosciences. In 1999, he coined the term "p-value fallacy" in a pair of landmark papers arguing for the adoption of Bayesian methods in medical research. He is a sibling of journalist and Democracy Now! host Amy Goodman and journalist David Goodman. == Education and career == Goodman received his AB in Biochemistry and Applied Mathematics from Harvard College in 1976, his MD from New York University School of Medicine, and his MHS and PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health, completing his doctoral dissertation Evidence and Clinical Trials in 1989 under the supervision of Richard Royall. He completed


id: harking-20260505-0
file: data/en.wikipedia.org/wiki/HARKing-0.md
category: reference
title: HARKing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HARKing
date_saved: 2026-05-05T03:42:30.957318+00:00
instance: kb-cron
parent: harking-20260505-0
summary:
HARKing (hypothesizing after the results are known) is an acronym coined by social psychologist Norbert Kerr that refers to the questionable research practice of "presenting a post hoc hypothesis in the introduction of a research report as if it were an a priori hypothesis". Hence, a key characteristic of HARKing is that post hoc hypothesizing is falsely portrayed as a priori hypothesizing. HARKing may occur when a researcher tests an a priori hypothesis but then omits that hypothesis from their research report after they find out the results of their test. Post hoc analysis or post hoc theorizing then may lead to a post hoc hypothesis. == Types == Several types of HARKing have been distinguished, including: THARKing Transparently hypothesizing after the results are known, rather than the secretive, undisclosed, HARKing that was first proposed by Kerr. In this case, researchers openly declare that they developed their hypotheses after they observed their research results. CHARKing (or Pure HARKing) CHARKing or "pure HARKing" refers to the practice of constructing new hypotheses after the results are known and presenting them as a priori hypotheses. CHARKing is often regarded as the prototypical form of HARKing. RHARKing RHARKing refers to retrieving old hypotheses from


id: icmje-recommendations-20260505-0
file: data/en.wikipedia.org/wiki/ICMJE_recommendations-0.md
category: reference
title: ICMJE recommendations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ICMJE_recommendations
date_saved: 2026-05-05T03:42:32.159920+00:00
instance: kb-cron
parent: icmje-recommendations-20260505-0
summary:
The ICMJE recommendations (full title, "Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals") are a set of guidelines produced by the International Committee of Medical Journal Editors for standardising the ethics, preparation and formatting of manuscripts submitted to biomedical journals for publication. Compliance with the ICMJE recommendations is required by most leading biomedical journals. Levels of real compliance are subject to debate. As of 9 January 2020, 5570 journals worldwide claim to follow the ICMJE recommendations. The recommendations were first issued in 1979 under the title "Uniform Requirements for Manuscripts Submitted to Biomedical Journals" (abbreviated URMs and often shortened to "Uniform Requirements"). After a series of revisions, they were given their current name in 2013. == International Committee of Medical Journal Editors == The International Committee of Medical Journal Editors (ICMJE) was originally known as the Vancouver Group, after the location of their first meeting in Vancouver, British Columbia in Canada. As of 2017 members of the ICMJE are: Annals of Internal Medicine BMJ Bulletin of the World Health Organization Deutsches Ärzteblatt Ethiopian Journal of Health Sciences Iranian Journal of Medical Sciences Journal of the American Medical Association (JAMA) New England Journal of Medicine


id: john-ioannidis-20260505-0
file: data/en.wikipedia.org/wiki/John_Ioannidis-0.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T03:42:37.127948+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
John P. A. Ioannidis ( EE-ə-NEE-diss; Greek: Ιωάννης Ιωαννίδης, pronounced [i.oˈanis i.oaˈniðis]; born 21 August 1965) is a Greek-American physician-scientist, writer, and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself (meta-research) primarily in clinical medicine and the social sciences. He has served on the editorial board of over twenty scientific journals including Journal of the American Medical Association (JAMA), Journal of the National Cancer Institute (JNCI), and The Lancet. Ioannidis's 2005 essay "Why Most Published Research Findings Are False" was the most-accessed article in the history of Public Library of Science (PLOS) as of 2020, with more than three million views. Ioannidis was a prominent opponent of lockdowns during the COVID-19 pandemic, and he has been accused of promoting conspiracy theories about COVID-19 policies and public health and safety measures. == Early life and education == Born in New York City in 1965, Ioannidis was raised in Athens, Greece. He was valedictorian of his class at Athens College, graduating in 1984, and won a number of awards, including the National Award of the Greek Mathematical Society. He graduated in the top rank of his class at the University of


id: john-ioannidis-20260505-1
file: data/en.wikipedia.org/wiki/John_Ioannidis-1.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T03:42:37.127948+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
=== Meta-analysis === Ioannidis has developed and popularized several methods for meta-analysis and has made several conceptual advances in this field. These include methods for assessing heterogeneity and its uncertainty, methods for meta-analysis involving multiple treatments, methods and processes for umbrella reviews, and several approaches to identifying bias and adjusting the results of meta-analyses for bias, such as publication bias and reporting bias resulting in funnel-plot asymmetry. He has also alerted about the misuse and misinterpretation of bias tests. Along with David Chavalarias, he catalogued 235 biases across the entire publication record of biomedical research. Ioannidis has been critical of flawed, misleading and redundant meta-analyses, estimating that few meta-analyses in medicine are both bias-free and clinically useful. He has performed empirical evaluations of the concordance of results between meta-analyses and large trials and between randomized trials and non-randomized studies. === Evidence-based medicine === Ioannidis has been one of the strong proponents and earlier advocates of evidence-based medicine. However, he has alerted that, over the years, as evidence-based medicine acquired more prominence and influence, it was hijacked to serve other agendas that are often biased. In an essay written to honor his late mentor David Sackett, he stated that: Influential randomized


id: john-ioannidis-20260505-2
file: data/en.wikipedia.org/wiki/John_Ioannidis-2.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T03:42:37.127948+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
=== Neuroscience === Along with colleagues, Ioannidis has performed empirical evaluations and meta-research assessments of large numbers of scientific studies in neuroscience and have found that lack of power is a very common problem, leading to both false-negatives (the inability to discover true signals) and false-positives (finding spurious signals). === Economics === In empirical assessments of all meta-analyses that have been conducted on economics topics, Ioannidis and colleagues have found that most of the studies in these fields are small and under-powered. Using bias detection and correction methods, they have concluded that nearly 80% of the reported effects in the empirical economics literature is exaggerated; typically by a factor of two, and with one-third inflated by a factor of four or more. == Editorial appointments == Ioannidis has served on the editorial board of a number of scientific journals, including the European Journal of Clinical Investigation (editor-in-chief, 2010–2019), BMC Medicine, International Journal of Epidemiology, Journal of the American Medical Association, Journal of Clinical Epidemiology, Journal of Infectious Diseases, International Journal of Molecular Epidemiology and Genetics, International Journal of Epidemiology, Journal of Translational Medicine, Journal of Evaluation in Clinical Practice, Clinical Chemistry, Physiological Reviews, Royal Society Open Science, Research Integrity and


id: john-ioannidis-20260505-3
file: data/en.wikipedia.org/wiki/John_Ioannidis-3.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T03:42:37.127948+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
== COVID-19 == In an editorial on STAT published March 17, 2020, Ioannidis wondered whether the global response to the COVID-19 pandemic may be a "once-in-a-century evidence fiasco" and asked for obtaining more reliable data to deal with the pandemic. He made a rough estimation that the coronavirus could cause 10,000 U.S. deaths if it infected 1% of the U.S. population, but argued that more data was needed to determine how widely the virus would spread. The virus in fact eventually became widely disseminated, and would cause more than one million deaths in the U.S. Ioannidis expressed doubt that vaccines or treatments would be developed and tested in time to affect how the pandemic would unfold. Marc Lipsitch, Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, objected to Ioannidis's characterization of the global response in a reply that was published on STAT the next day after Ioannidis's. In March 2020, Ioannidis tried to organize a meeting at the White House where he and colleagues would caution President Donald Trump against "shutting down the country for [a] very long time and jeopardizing so many lives in doing this", according to a proposal


id: john-ioannidis-20260505-4
file: data/en.wikipedia.org/wiki/John_Ioannidis-4.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T03:42:37.127948+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
== Reception == In the 2000s and 2010s, during a period of regular publications from Ioannidis on the replication crisis in science, observers in the popular press commented that Ioannidis "may be one of the most influential scientists alive", and was "cementing his role as one of medicine's top mythbusters". In 2014, The Economist featured Ioannidis and Steven Goodman in an article on the Meta-Research Innovation Center at Stanford, and George Johnson of the New York Times wrote an article on the importance of reproducible research, profiling Ioannidis's two 2005 papers as playing a critical role in raising concern about the issue in the scientific community, as later expressed by the journal Nature. This acclaim continued into the late 2010s, with Wired mentionining Ioannidis as "arguably the replication crisis' chief inquisitor". His research on replicability reached multiple fields, including the specious statistics behind some drug subscriptions, and findings from Ioannidis that only a minority of widely cited health research studies carried out over the last decade could be replicated, with at least 1 in 6 actually being contradicted by later studies,. Elsevier featured his analogy of reproducibility in research to "taming a complex beast". He was also an early critic


id: journalology-20260505-0
file: data/en.wikipedia.org/wiki/Journalology-0.md
category: reference
title: Journalology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Journalology
date_saved: 2026-05-05T03:42:38.295924+00:00
instance: kb-cron
parent: journalology-20260505-0
summary:
Journalology, also referred to as publication science, is the scholarly study of all aspects of the academic publishing process. The field seeks to improve the quality of scholarly research by implementing evidence-based practices in academic publishing. The term "journalology" was coined by Stephen Lock, the former editor-in-chief of the BMJ. The first Peer Review Congress, held in 1989 in Chicago, Illinois, is considered a pivotal moment in the founding of journalology as a distinct field. The field of journalology has exerted a significant influence in promoting the adoption of pre-registration in scientific research, particularly in the domain of clinical trials. Clinical trial registration is now expected in most countries. Journalology researchers also work to reform the peer review process. == History == The earliest scientific journals were founded in the seventeenth century. While most early journals used peer review, peer review did not become common practice in medical journals until after World War II. The scholarly publishing process (including peer review) did not arise by scientific means and still suffers from problems with reliability (consistency and dependability), such as a lack of uniform standards and validity (well-founded, efficacious). Attempts to reform the academic publishing practice began to gain traction in


id: open-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Open_peer_review-0.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:42:39.491508+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Open peer review is the various possible modifications of the traditional scholarly peer review process. The three most common modifications to which the term is applied are: Open identities: Authors and reviewers are aware of each other's identity. Open reports: Review reports are published alongside the relevant article (rather than being kept confidential). Open participation: The wider community (and not just invited reviewers) are able to contribute to the review process. These modifications are supposed to address various perceived shortcomings of the traditional scholarly peer review process, in particular its lack of transparency, lack of incentives, wastefulness, bullying and harassment. == Definitions == Open identities Open peer review may be defined as "any scholarly review mechanism providing disclosure of author and referee identities to one another at any point during the peer review or publication process". Then reviewer's identities may or may not be disclosed to the public. This is in contrast to the traditional peer review process where reviewers remain anonymous to anyone but the journal's editors. Authors' names are disclosed during the process in a single-blind organisation of reviews. In the double-blind process, authors' names and reviewers' names all remain anonymous except to the editor. Open reports Open


id: open-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Open_peer_review-1.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:42:39.491508+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
== History == In 1997, the open participation journal Electronic Transactions on Artificial Intelligence was launched. Articles were first "published" (meaning the day when the article is first made public), then "reviewed" (meaning a open process of public discussion), and then "refereed" (meaning the confidential pass/fail decision of whether to accept the article or not). In 1999, the open access journal Journal of Medical Internet Research was launched, which from its inception decided to publish the names of the reviewers at the bottom of each published article. Also in 1999, the British Medical Journal moved to an open peer review system, revealing reviewers' identities to the authors but not the readers, and in 2000, the medical journals in the open access BMC series published by BioMed Central, launched using open peer review. As with the BMJ, the reviewers' names are included on the peer review reports. In addition, if the article is published the reports are made available online as part of the "pre-publication history"'. Several other journals published by the BMJ Group allow optional open peer review, as does PLoS Medicine, published by the Public Library of Science. The BMJ's Rapid Responses allows ongoing debate and criticism following publication.


id: open-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Open_peer_review-2.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:42:39.491508+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Publishers: BioMed Central BMJ Group Copernicus Publications European Molecular Biology Organization (EMBO) Frontiers MDPI (authors have the option to publish peer review reports, etc.) Nature Research Open Research Europe (the European Union publishing platform for Horizon Europe and Horizon 2020 projects) Journals: eLife GigaScience Nature PeerJ PLOS ReScience C Semantic Web journal by IOS Press WikiJournal SciPost Peer review at The BMJ, BioMed Central, EMBO, eLife, ReScience C, and the Semantic Web journal involves posting the entire pre-publication history of the article online, including not only signed reviews of the article, but also its previous versions and in some cases names of handling editors and author responses to the reviewers. Furthermore, the Semantic Web journal publishes reviews of all submissions, including rejected ones, on its website, while eLife plans to publish the reviews not only for published articles, but also for rejected articles. The European Geosciences Union operates public discussions where open peer review is conducted before suitable articles are accepted for publication in the journal. Sci, an open access journal which covers all research fields, adapted a post publication public peer-review (P4R) in which it promised authors immediate visibility of their manuscripts on the journal's online platform after a


id: open-science-20260505-0
file: data/en.wikipedia.org/wiki/Open_science-0.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
Open science (also known as open research) is the movement to make scientific research, including publications, data, physical samples, software, and models, transparent and accessible to all levels of society through collaborative networks. This encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open-notebook science (such as openly sharing data and code), broader dissemination and public engagement in science, and generally making it easier to publish, access, and communicate scientific knowledge. Usage of the term varies substantially across disciplines, with a notable prevalence in the STEM disciplines. The term 'open research' has gained currency as a broader alternative to 'open science,' encompassing the humanities and arts alongside traditional scientific disciplines. The primary focus connecting all disciplines is the widespread uptake of new technologies and tools, and the underlying ecology of the production, dissemination and reception of knowledge from a research-based point-of-view. The term 'open scholarship' has also been proposed to include research from the arts and humanities as well as the different roles and practices that researchers perform as educators and communicators. Open science can be seen as continuing, rather than revolutionizing, practices that began in the 17th century with the academic journal, which


id: open-science-20260505-1
file: data/en.wikipedia.org/wiki/Open_science-1.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
Peer review is widely acknowledged as time-consuming. Citation impact, attributed to the authors, correlates more closely with journal circulation than with article quality. Open Science–aligned publishing formats rarely conform to traditional journal structures that calculate impact factors. Hence, this school argues that there are faster impact measurement technologies that can account for a range of publication types as well as social media web coverage of a scientific contribution to arrive at a complete evaluation of how impactful the science contribution was. The gist of the argument for this school is that hidden uses like reading, bookmarking, sharing, discussing and rating are traceable activities, and these traces can and should be used to develop a newer measure of scientific impact. The umbrella jargon for this new type of impact measurements is called altmetrics, coined in a 2011 article by Priem et al., (2011). Markedly, the authors discuss evidence that altmetrics differ from traditional webometrics which are slow and unstructured. Altmetrics are proposed to rely upon a greater set of measures that account for tweets, blogs, discussions, and bookmarks. Scholars propose that altmetrics should capture the entire research lifecycle, including collaboration patterns, to produce comprehensive impact measures. However, the authors are explicit


id: open-science-20260505-2
file: data/en.wikipedia.org/wiki/Open_science-2.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
=== Before journals === Before the advent of scientific journals, scientists had little to gain and much to lose by publicizing scientific discoveries. Many scientists, including Galileo, Kepler, Isaac Newton, Christiaan Huygens, and Robert Hooke, made claim to their discoveries by describing them in papers coded in anagrams or cyphers and then distributing the coded text. Their intent was to develop their discovery into something off which they could profit, then reveal their discovery to prove ownership when they were prepared to make a claim on it. The system of not publicizing discoveries caused problems because discoveries were not shared quickly and because it sometimes was difficult for the discoverer to prove priority. Newton and Gottfried Leibniz both claimed priority in discovering calculus. Newton said that he wrote about calculus in the 1660s and 1670s, but did not publish until 1693. Leibniz published "Nova Methodus pro Maximis et Minimis", a treatise on calculus, in 1684. Debates over priority are inherent in systems where science is not published openly, and this was problematic for scientists who wanted to benefit from priority. Under aristocratic patronage, scientists received funding to develop useful innovations or provide entertainment, creating pressure to satisfy patrons' desires and


id: open-science-20260505-3
file: data/en.wikipedia.org/wiki/Open_science-3.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
=== Internet and the free access to scientific documents === The open science movement, as presented in activist and institutional discourses at the beginning of the 21st century, refers to different ways of opening up science, especially in the Internet age. Its first pillar is free access to scientific publications. This issue entered the political landscape when the Budapest Open Access Initiative was released February 14, 2002, following a conference organized by the Open Society Institute (now Open Society Foundations) on December 1–2, 2001. The resulting declaration calls for the use of digital tools such as open archives and open access journals, free of charge for the reader. The idea of open access to scientific publications quickly became inseparable from the question of free licenses to guarantee the right to disseminate and possibly modify shared documents, such as the Creative Commons licenses, created in 2002. In 2011, a new text from the Budapest Open Initiative explicitly refers to the relevance of the CC-BY license to guarantee free dissemination and not only free access to a scientific document. Beyond publications, the open access principle has expanded to include research data — the empirical foundation of scientific studies across disciplines, as mentioned


id: open-science-20260505-4
file: data/en.wikipedia.org/wiki/Open_science-4.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
== Open Science and Research Assessment == A central aspect of the Open Science movement is the reform of research assessment. Initiatives such as the Coalition for Advancing Research Assessment (CoARA) (launched in 2022) and the San Francisco Declaration on Research Assessment (DORA) advocate moving away from traditional quantitative metrics like the Journal Impact Factor (JIF) and the h-Index, as these often exhibit biases and neglect qualitative aspects. Instead, alternative metrics and indicators, such as altmetrics and Open Science indicators, are to be given greater consideration. Open Science indicators include metrics such as the number of open access publications, data management plans, preprints, FAIR-licensed data, and open peer review reports. These approaches aim to promote the transparency and reusability of scientific outcomes, thereby enabling a fairer and more comprehensive evaluation of scientific achievements.While Open Science aims to enhance transparency, accessibility, and collaboration, the introduction of numerous new metrics to measure openness has led to unintended consequences. These metrics often rely on quantitative indicators, which conflict with the holistic and qualitative approaches advocated by initiatives such as CoARA and DORA. The core issue is that these metrics are designed not only to measure but also to influence researchers' behavior. This can


id: open-science-20260505-5
file: data/en.wikipedia.org/wiki/Open_science-5.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
=== Disadvantages === Arguments against open science tend to focus on the advantages of data ownership and concerns about the misuse of data, but see. Other concerns around data misuse involve privacy and safety risks that arise from ecological data on protected animal populations or sensitive data on human specimens that could potentially be re-identified and lead to hard and stigma for certain populations. Potential misuse Allowing open access can bring documented cases of misuse, and such misuse can take various forms from accidental errors to intentional forms of misuse like misrepresenting data in order to manipulate or deceive. In 2011, Dutch researchers announced their intention to publish a research paper in the journal Science describing the creation of a strain of H5N1 influenza which can be easily passed between ferrets, the mammals which most closely mimic the human response to the flu. The announcement triggered a controversy in both political and scientific circles about the ethical implications of publishing scientific data which could be used to create biological weapons. These events are examples of how science data could potentially be misused. It has been argued that constraining the dissemination of dual-use knowledge can in certain cases be justified because,


id: open-science-20260505-6
file: data/en.wikipedia.org/wiki/Open_science-6.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
=== Open-science projects === Different projects conduct, advocate, develop tools for, or fund open science. The Allen Institute for Brain Science conducts numerous open science projects while the Center for Open Science has projects to conduct, advocate, and create tools for open science. Other workgroups have been created in different fields, such as the Decision Analysis in R for Technologies in Health (DARTH) workgroup], which is a multi-institutional, multi-university collaborative effort by researchers who have a common goal to develop transparent and open-source solutions to decision analysis in health. Organizations have extremely diverse sizes and structures. The Open Knowledge Foundation (OKF) is a global organization sharing large data catalogs, running face to face conferences, and supporting open source software projects. In contrast, Blue Obelisk is an informal group of chemists and associated cheminformatics projects. The tableau of organizations is dynamic with some organizations becoming defunct, e.g., Science Commons, and new organizations trying to grow, e.g., the Self-Journal of Science. Common organizing forces include the knowledge domain, type of service provided, and even geography, e.g., OCSDNet's concentration on the developing world. The Allen Brain Atlas maps gene expression in human and mouse brains; the Encyclopedia of Life documents all the terrestrial


id: open-science-20260505-7
file: data/en.wikipedia.org/wiki/Open_science-7.md
category: reference
title: Open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science
date_saved: 2026-05-05T03:42:40.702535+00:00
instance: kb-cron
parent: open-science-20260505-0
summary:
F1000Research provides open publishing and open peer review for the life sciences. The Open Library of Humanities is a non-profit open access publisher for the humanities and social sciences. The Journals Library of the National Institute for Health and Care Research (NIHR) publishes all relevant documents and data from the onset of research projects, updating them alongside the progress of the study. Journal support for open-science does not conflict with preprint servers: figshare archives and shares images, readings, and other data; and Open Science Framework preprints, arXiv, and HAL Archives Ouvertes provide electronic preprints across many fields. === Software === A variety of computer resources support open science. These include software like the Open Science Framework from the Center for Open Science to manage project information, data archiving and team coordination; distributed computing services like Ibercivis to use unused CPU time for computationally intensive tasks; and services like Experiment.com to provide crowdsourced funding for research projects. Blockchain platforms for open science have been proposed. The first such platform is the Open Science Organization, which aims to solve urgent problems with fragmentation of the scientific ecosystem and difficulties of producing validated, quality science. Among the initiatives of Open Science Organization include


id: openneuro-20260505-0
file: data/en.wikipedia.org/wiki/OpenNeuro-0.md
category: reference
title: OpenNeuro
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OpenNeuro
date_saved: 2026-05-05T03:42:41.848899+00:00
instance: kb-cron
parent: openneuro-20260505-0
summary:
OpenNeuro (originally OpenfMRI) is an open-science neuroinformatics database storing datasets from human brain imaging research studies. The database is available online. OpenNeuro accepts datasets formatted from brain imaging research according to a community-developed standard, and uploaded datasets are made available with minimal restrictions to the public at large. The Research Resource Identifier for OpenNeuro is SCR_005031. Datasets that cannot be shared openly are not currently supported. The research group that runs OpenNeuro described the system in a Frontiers in Neuroinformatics article in 2013 and a NeuroImage article in 2015. As of 2025, the National Institutes of Health has awarded OpenNeuro over 8 million dollars in research funding, and researchers have mentioned OpenNeuro in over 200 articles in open access literature. == History == The Neuroimaging Informatics Tools and Resources Clearinghouse made available two other online neuroimaging databases, which both predated OpenNeuro. One was the fMRI Data Center (fMRIDC), which collected similar data but distributed via physical media. The fMRIDC no longer accepts data submissions. The other repository was the 1000 Functional Connectomes Project, which collected data from resting-state fMRI studies. In February 2018, the research group behind OpenfMRI renamed the database to OpenNeuro to reflect broader range of accepted data


id: scholarly-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-0.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review. Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process


id: scholarly-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-1.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals. Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion. Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material. Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing). Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an


id: scholarly-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-2.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
to unconditionally accept the manuscript or the proposal, to accept it in the event that its authors improve it in certain ways to reject it, but encourage revision and invite re-submission to reject it outright. During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments. In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and


id: scholarly-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-3.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
== Recruiting referees == At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications. Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest). Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in


id: scholarly-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-4.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Internal and external peer review === External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit. Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike


id: scholarly-peer-review-20260505-5
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-5.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Transparent peer review === In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review. === Pre- and post-publication peer review === The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal. ==== Pre-publication peer review ==== Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public,


id: scholarly-peer-review-20260505-6
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-6.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977. Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version. Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with


id: scholarly-peer-review-20260505-7
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-7.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations. Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process. To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review


id: scholarly-peer-review-20260505-8
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-8.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Peer review and trust === Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature. Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate


id: scholarly-peer-review-20260505-9
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-9.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
The belief that scholars are incapable of evaluating the quality of work on their own, that they are in need of a gatekeeper to inform them of what is good and what is not. The belief that scholars need a "guardian" to make sure they are doing good work. Others argue that authors most of all have a vested interest in the quality of a particular piece of work. Only the authors could have, as Feynman (1974) puts it, the "extra type of integrity that is beyond not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist." If anything, the current peer review process and academic system could penalize, or at least fail to incentivize, such integrity. Instead, the credibility conferred by the "peer-reviewed" label could diminish what Feynman calls the culture of doubt necessary for science to operate a self-correcting, truth-seeking process. The effects of this can be seen in the ongoing replication crisis, hoaxes, and widespread outrage over the inefficacy of the current system. It's common to think that more oversight is the answer, as peer reviewers are not at all lacking in skepticism. But the


id: scholarly-peer-review-20260505-10
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-10.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Allegations of bias and suppression === The interposition of editors and reviewers between authors and readers may enable the intermediators to act as gatekeepers. Some sociologists of science argue that peer review makes the ability to publish susceptible to control by elites and to personal jealousy. The peer review process may sometimes impede progress and may be biased against novelty. A linguistic analysis of review reports suggests that reviewers focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. Reviewers tend to be especially critical of conclusions that contradict their own views, and lenient towards those that match them. At the same time, established scientists are more likely than others to be sought out as referees, particularly by high-prestige journals/publishers. As a result, ideas that harmonize with the established experts' are more likely to see print and to appear in premier journals than are iconoclastic or revolutionary ones. This accords with Thomas Kuhn's well-known observations regarding scientific revolutions. A theoretical model has been established whose simulations imply that peer review and over-competitive research funding foster mainstream opinion to monopoly. Criticisms of traditional anonymous peer review


id: scholarly-peer-review-20260505-11
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-11.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:42:43.135775+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
==== Plagiarism ==== Reviewers generally lack access to raw data, but do see the full text of the manuscript, and are typically familiar with recent publications in the area. Thus, they are in a better position to detect plagiarism of prose than fraudulent data. A few cases of such textual plagiarism by historians, for instance, have been widely publicized. On the scientific side, a poll of 3,247 scientists funded by the U.S. National Institutes of Health found 0.3% admitted faking data and 1.4% admitted plagiarism. Additionally, 4.7% of the same poll admitted to self-plagiarism or autoplagiarism, in which an author republishes the same material, data, or text, without citing their earlier work. Self-plagiarisms are less likely to be detected in double-blinded peer reviews. ==== Examples ==== "Perhaps the most widely recognized failure of peer review is its inability to ensure the identification of high-quality work. The list of important scientific papers that were rejected by some peer-reviewed journals goes back at least as far as the editor of Philosophical Transaction's 1796 rejection of Edward Jenner's report of the first vaccination against smallpox." The Soon and Baliunas controversy involved the publication in 2003 of a review study written by aerospace engineer


id: preregistration-science-20260505-0
file: data/en.wikipedia.org/wiki/Preregistration_(science)-0.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
Preregistration is the practice of registering the hypotheses, methods, or analyses of a scientific study before it is conducted. Clinical trial registration is similar, although it may not require the registration of a study's analysis protocol. Finally, registered reports include the peer review and in principle acceptance of a study protocol prior to data collection. Preregistration has the goal to transparently evaluate the severity of hypothesis tests, and can have a number of secondary goals (which can also be achieved without preregistering ), including (a) facilitating and documenting research plans, (b) identifying and reducing questionable research practices and researcher biases, (c) distinguishing between confirmatory and exploratory analyses, and, in the case of Registered Reports, (d) facilitating results-blind peer review, and (e) reducing publication bias. Although the idea of preregistration is old, the practice of preregistering studies has gained prominence to mitigate certain issues that contribute to the replication crisis in scientific studies. Among others, these issues include publication bias and questionable research practices, such as p-hacking and HARKing. == Types == === Standard preregistration === In the standard preregistration format, researchers prepare a research protocol document prior to conducting their research. Ideally, this document indicates the research hypotheses, sampling procedure,


id: preregistration-science-20260505-1
file: data/en.wikipedia.org/wiki/Preregistration_(science)-1.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Creation and development === For many years, scientists and others have worried about reporting biases such that negative or null results from initiated clinical trials may be less likely to be published than positive results, thus skewing the literature and our understanding of how well interventions work. This worry has been international and written about for over 50 years. One of the proposals to address this potential bias was a comprehensive register of initiated clinical trials that would inform the public which trials had been started. Ethical issues were those that seemed to interest the public most, as trialists (including those with potential commercial gain) benefited from those who enrolled in trials, but were not required to "give back," telling the public what they had learned. Those who were particularly concerned by the double standard were systematic reviewers, those who summarize what is known from clinical trials. If the literature is skewed, then the results of a systematic review are also likely to be skewed, possibly favoring the test intervention when in fact the accumulated data do not show this, if all data were made public. ClinicalTrials.gov was originally developed largely as a result of breast cancer consumer lobbying,


id: preregistration-science-20260505-2
file: data/en.wikipedia.org/wiki/Preregistration_(science)-2.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
== Benefits == Several articles have outlined the rationale for preregistration (e.g., Lakens, 2019; Nosek et al., 2018; Wagenmakers et al., 2012). The primary goal of preregistration is to improve the transparency of reported hypothesis tests, which allows readers to evaluate the extent to which decisions during the data analysis were pre-planned (maintaining statistical error control) or data-driven (increasing the Type 1 or Type 2 error rate). Meta-scientific research has revealed additional benefits. Researchers indicate preregistering a study leads to a more carefully thought through research hypothesis, experimental design, and statistical analysis. In addition, preregistration has been shown to encourage better learning of Open Science concepts and students felt that they understood their dissertation and it improved the clarity of the manuscript writing, promoted rigour and were more likely to avoid questionable research practices. In addition, it becomes a tool that supervisors can use to shape students to combat any questionable research practices. A 2024 study in the Journal of Political Economy: Microeconomics preregistration in economics journals found that preregistration reduced p-hacking and publication bias if the preregistration was accompanied by a preanalysis plan, but not if the preregistration did not specify the planned analyses. == Criticisms == === Analytical


id: preregistration-science-20260505-3
file: data/en.wikipedia.org/wiki/Preregistration_(science)-3.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Practical Implementation === There are also concerns about the practical implementation of preregistration. Many preregistered protocols leave plenty of room for p-hacking (Bakker et al., 2020; Heirene et al., 2021; Ikeda et al., 2019; Singh et al., 2021; Van den Akker et al., 2023), and researchers rarely follow the exact research methods and analyses that they preregister (Abrams et al., 2020; Claesen et al., 2019; Heirene et al., 2021; Clayson et al., 2025; see also Boghdadly et al., 2018; Singh et al., 2021; Sun et al., 2019). In terms of credibility, pre-registered studies are only of higher quality than non-pre-registered studies if the former has a power analysis and higher sample size than the latter but other than that they do not seem to prevent p-hacking and HARKing, as both the proportion of positive results and effect sizes are similar between preregistered and non-preregistered studies (Van den Akker et al., 2023). In terms of adherence, a study of 92 EEG/ERP studies showed that only 60% of studies adhered to their preregistrations or disclosed all deviations. Notably, registered reports had the higher adherence rates (92%) than unreviewed preregistrations (60%). In general, around three-quarters of preregistered studies included at least one


id: publication-bias-20260505-0
file: data/en.wikipedia.org/wiki/Publication_bias-0.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T03:42:45.581729+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience. Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging. Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results. Attempts to find unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require that authors preregister their methods


id: publication-bias-20260505-1
file: data/en.wikipedia.org/wiki/Publication_bias-1.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T03:42:45.581729+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
Meta-analyses and systematic reviews can account for publication bias by including evidence from unpublished studies and the grey literature. The presence of publication bias can also be explored by constructing a funnel plot in which the estimate of the reported effect size is plotted against a measure of precision or sample size. The premise is that the scatter of points should reflect a funnel shape, indicating that the reporting of effect sizes is not related to their statistical significance. However, when small studies are predominately in one direction (usually the direction of larger effect sizes), asymmetry will ensue and this may be indicative of publication bias. Because an inevitable degree of subjectivity exists in the interpretation of funnel plots, several tests have been proposed for detecting funnel plot asymmetry. These are often based on linear regression including the popular Eggers regression test, and may adopt a multiplicative or additive dispersion parameter to adjust for the presence of between-study heterogeneity. Some approaches may even attempt to compensate for the (potential) presence of publication bias, which is particularly useful to explore the potential impact on meta-analysis results. In ecology and environmental biology, a study found that publication bias impacted the effect size,


id: replication-crisis-20260505-0
file: data/en.wikipedia.org/wiki/Replication_crisis-0.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The replication crisis, also known as the reproducibility or replicability crisis, refers to widespread failures to reproduce published scientific results. Because the reproducibility of empirical results is the cornerstone of the scientific method, such failures undermine the credibility of theories and challenge substantial parts of scientific knowledge. Psychology and medicine have been focal points for replication efforts, with researchers systematically reexamining classic studies to verify their reliability and, when failures emerge, to identify the underlying causes. Data strongly indicates that other natural and social sciences are also affected. The phrase "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Considerations of causes and remedies have given rise to a new scientific discipline known as metascience, which uses methods of empirical research to examine empirical research practice. Researchers distinguish two forms of reproducibility. Reproducibility in a narrow sense refers to reexamining and validating the analysis of a set of data. The second category, replication, involves repeating an experiment or study with new, independent data to verify the original conclusions. == Background == === Replication === Replication has been called "the cornerstone of science". Environmental health scientist Stefan Schmidt began a 2009 review with


id: replication-crisis-20260505-1
file: data/en.wikipedia.org/wiki/Replication_crisis-1.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
A null hypothesis test is a decision procedure which takes in some data, and outputs either H 0 {\displaystyle H_{0}} or H 1 {\displaystyle H_{1}} . If it outputs H 1 {\displaystyle H_{1}} , it is usually stated as "there is a statistically significant effect" or "the null hypothesis is rejected". Often, the statistical test is a (one-sided) threshold test, which is structured as follows: Gather data D {\displaystyle D} . Compute a test statistic t [ D ] {\displaystyle t[D]} for the data. Compare the test statistic against a critical value/threshold t threshold {\displaystyle t_{\text{threshold}}} . If t [ D ] > t threshold {\displaystyle t[D]>t_{\text{threshold}}} , then output H 1 {\displaystyle H_{1}} , else, output H 0 {\displaystyle H_{0}} . A two-sided threshold test is similar, but with two thresholds, such that it outputs H 1 {\displaystyle H_{1}} if either t [ D ] < t threshold − {\displaystyle t[D]<t_{\text{threshold}}^{-}} or t [ D ] > t threshold + {\displaystyle t[D]>t_{\text{threshold}}^{+}} There are 4 possible outcomes of a null hypothesis test: false negative, true negative, false positive, true positive. A false negative means that H 0 {\displaystyle H_{0}} is true, but the test outcome is H 1 {\displaystyle


id: replication-crisis-20260505-2
file: data/en.wikipedia.org/wiki/Replication_crisis-2.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Social priming failures: In the early 2010s, two direct replication attempts failed to reproduce results from social psychologist John Bargh's much-cited "elderly-walking" study (originally published in 1996). This experiment was part of a series of three studies that had been widely cited throughout the years, was regularly taught in university courses, and had inspired many conceptual replications. These replication failures triggered intense disagreement between replication researchers and the original authors. Notably, many of the conceptual replications of the original studies also failed to replicate in subsequent direct replications. Experiments on extrasensory perception: Social psychologist Daryl Bem conducted a series of experiments supposedly providing evidence for the controversial phenomenon of extrasensory perception. Bem faced substantial criticism of his study's methodology. Reanalysis of his data found no evidence for extrasensory perception. The experiment also failed to replicate in subsequent direct replications. According to Romero, what the community found particularly upsetting was that many of the flawed procedures and statistical tools used in Bem's studies were part of common research practice in psychology. Biomedical replication failures: Scientists from biotech companies Amgen and Bayer Healthcare reported alarmingly low replication rates (11–20%) of landmark findings in preclinical oncological research. P-hacking studies and questionable research practices:


id: replication-crisis-20260505-3
file: data/en.wikipedia.org/wiki/Replication_crisis-3.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== In psychology === Several factors have combined to put psychology at the center of the conversation. Some areas of psychology once considered solid, such as social priming and ego depletion, have come under increased scrutiny due to failed replications. Much of the focus has been on social psychology, although other areas of psychology such as clinical psychology, developmental psychology, and educational research have also been implicated. In August 2015, the first open empirical study of reproducibility in psychology was published, called The Reproducibility Project: Psychology. Coordinated by psychologist Brian Nosek, researchers redid 100 studies in psychological science from three high-ranking psychology journals (Journal of Personality and Social Psychology, Journal of Experimental Psychology: Learning, Memory, and Cognition, and Psychological Science). Of 97 original studies with significant effects, 36% replicated successfully (p value below 0.05), with effect sizes averaging half the original magnitude. Among non-replications, 25% directly contradicted the original while 49% were inconclusive due to underpowered designs. The same paper examined the reproducibility rates and effect sizes by journal and discipline. Study replication rates were 23% for the Journal of Personality and Social Psychology, 48% for Journal of Experimental Psychology: Learning, Memory, and Cognition, and 38% for Psychological Science. Studies


id: replication-crisis-20260505-4
file: data/en.wikipedia.org/wiki/Replication_crisis-4.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Across fields === A 2016 survey by Nature on 1,576 researchers who took a brief online questionnaire on reproducibility found that more than 70% of researchers have tried and failed to reproduce another scientist's experiment results (including 87% of chemists, 77% of biologists, 69% of physicists and engineers, 67% of medical researchers, 64% of earth and environmental scientists, and 62% of all others), and more than half have failed to reproduce their own experiments. But fewer than 20% had been contacted by another researcher unable to reproduce their work. The survey found that fewer than 31% of researchers believe that failure to reproduce results means that the original result is probably wrong, although 52% agree that a significant replication crisis exists. Most researchers said they still trust the published literature. In 2010, Fanelli (2010) found that 91.5% of psychiatry/psychology studies confirmed the effects they were looking for, and concluded that the odds of this happening (a positive result) was around five times higher than in fields such as astronomy or geosciences. Fanelli argued that this is because researchers in "softer" sciences have fewer constraints to their conscious and unconscious biases. Early analysis of result-blind peer review, which is less


id: replication-crisis-20260505-5
file: data/en.wikipedia.org/wiki/Replication_crisis-5.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Academic "publish or perish" culture exacerbates publication bias. Intense pressure to publish in recognized journals, driven by hypercompetitive environments and bibliometric career evaluations, incentivizes researchers to prioritize publishable results over validity. According to Fanelli, this pushes scientists to employ a number of strategies aimed at making results "publishable". In the context of publication bias, this can mean adopting behaviors aimed at making results positive or statistically significant, often at the expense of their validity. According to Center for Open Science founder Brian Nosek and his colleagues, "publish or perish" culture created a situation whereby the goals and values of single scientists (e.g., publishability) are not aligned with the general goals of science (e.g., pursuing scientific truth). This is detrimental to the validity of published findings. Philosopher Brian D. Earp and psychologist Jim A. C. Everett argue that, although replication is in the best interests of academics and researchers as a group, features of academic psychological culture discourage replication by individual researchers. They argue that performing replications can be time-consuming, and take away resources from projects that reflect the researcher's original thinking. They are harder to publish, largely because they are unoriginal, and even when they can be published they are


id: replication-crisis-20260505-6
file: data/en.wikipedia.org/wiki/Replication_crisis-6.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== In medicine ==== Irreproducible medical studies commonly share these characteristics: investigators not being blinded to the experimental versus the control arms; failure to repeat experiments; lack of positive and negative controls; failing to report all the data; inappropriate use of statistical tests; and use of reagents that were not appropriately validated. ==== In AI research ==== In machine learning research, a range of questionable practices have emerged due to intense pressure to achieve state-of-the-art benchmark results. Common evaluation questionable practices include "benchmark overfitting" by repeatedly tuning hyperparameters on held-out test sets, selectively reporting the best of multiple random seeds or experimental runs, and "metric hacking" through unreported post hoc decisions such as choice of tokenization or evaluation scripts that inflate scores. Researchers also frequently cherry-pick tasks or datasets where their proposed model outperforms baselines, omit negative or lower-performing results, and obscure compute budgets to present energy-intensive methods as efficient. Such practices can give a misleading impression of model progress and hinder fair comparisons across methods. Moreover, many machine learning studies suffer from irreproducible research practices that impede replication and auditing. Key issues include incomplete disclosure of training details—such as dataset preprocessing, exact model hyperparameters, random seeds, and hardware configurations—and


id: replication-crisis-20260505-7
file: data/en.wikipedia.org/wiki/Replication_crisis-7.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Statistical issues === ==== Low statistical power ==== Low statistical power hinders replication for three reasons: (1) low-power replications have reduced ability to detect true effects, (2) low-power original studies produce biased effect size estimates leading to undersized replications, and (3) low-power original studies yield results unlikely to reflect true effects. Mathematically, the probability of replicating a previous publication that rejected a null hypothesis H 0 {\displaystyle H_{0}} in favor of an alternative H 1 {\displaystyle H_{1}} is ( significance ) P r ( H 0 | publication ) + ( power ) P r ( H 1 | publication ) ≤ ( power ) {\displaystyle ({\text{significance}})Pr(H_{0}|{\text{publication}})+({\text{power}})Pr(H_{1}|{\text{publication}})\leq ({\text{power}})} assuming significance is less than power. Thus, low power implies low probability of replication, regardless of how the previous publication was designed, and regardless of which hypothesis is really true. Stanley and colleagues estimated the average statistical power of psychological literature by analyzing data from 200 meta-analyses. They found that on average, psychology studies have between 33.1% and 36.4% statistical power. These values are quite low compared to the 80% considered adequate statistical power for an experiment. Across the 200 meta-analyses, the median of studies with adequate statistical power was between


id: replication-crisis-20260505-8
file: data/en.wikipedia.org/wiki/Replication_crisis-8.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Various statistical methods can be applied to make the p-value appear smaller than it really is. This need not be malicious, as moderately flexible data analysis, routine in research, can increase the false-positive rate to above 60%. For example, if one collects some data, applies several different significance tests to it, and publishes only the one that happens to have a p-value less than 0.05, then the total p-value for "at least one significance test reaches p < 0.05" can be much larger than 0.05, because even if the null hypothesis were true, the probability that one out of many significance tests is extreme is not itself extreme. Typically, a statistical study has multiple steps, with several choices at each step, such as during data collection, outlier rejection, choice of test statistic, choice of one-tailed or two-tailed test, etc. These choices in the "garden of forking paths" multiply, creating many "researcher degrees of freedom". The effect is similar to the file-drawer problem, as the paths not taken are not published. Consider a simple illustration. Suppose the null hypothesis is true, and we have 20 possible significance tests to apply to the dataset. Also suppose the outcomes to the significance tests


id: replication-crisis-20260505-9
file: data/en.wikipedia.org/wiki/Replication_crisis-9.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Statistical heterogeneity ==== As also reported by Stanley and colleagues, a further reason studies might fail to replicate is high heterogeneity of the to-be-replicated effects. Heterogeneity (variance in research findings due to multiple true effect sizes rather than one) is measured by the I-squared statistic, which quantifies unexplained variation in effect sizes across studies. This variation can be due to differences in experimental methods, populations, cohorts, and statistical methods between replication studies. Heterogeneity poses a challenge to studies attempting to replicate previously found effect sizes. When heterogeneity is high, subsequent replications have a high probability of finding an effect size radically different than that of the original study. Importantly, significant levels of heterogeneity are also found in direct/exact replications of a study. Stanley and colleagues discuss this while reporting a study by quantitative behavioral scientist Richard Klein and colleagues, where the authors attempted to replicate 15 psychological effects across 36 different sites in Europe and the U.S. In the study, Klein and colleagues found significant amounts of heterogeneity in 8 out of 16 effects (I-squared = 23% to 91%). Importantly, while the replication sites intentionally differed on a variety of characteristics, such differences could account for very little heterogeneity


id: replication-crisis-20260505-10
file: data/en.wikipedia.org/wiki/Replication_crisis-10.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
and the probability of replicating the statistical study is P r ( replication | find H 1 ) = P r ( find H 1 | H 1 ) P r ( H 1 | find H 1 ) + P r ( find H 1 | H 0 ) P r ( H 0 | find H 1 ) {\displaystyle Pr({\text{replication}}|{\text{ find }}H_{1})=Pr({\text{find }}H_{1}|H_{1})Pr(H_{1}|{\text{ find }}H_{1})+Pr({\text{find }}H_{1}|H_{0})Pr(H_{0}|{\text{ find }}H_{1})} which is also different from P r ( H 1 | find H 1 ) {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})} . In particular, for a fixed level of significance, the probability of replication increases with power, and prior probability for H 1 {\displaystyle H_{1}} . If the prior probability for H 1 {\displaystyle H_{1}} is small, then one would require a high power for replication. For example, if the prior probability of the null hypothesis is P r ( H 0 ) = 0.9 {\displaystyle Pr(H_{0})=0.9} , and the study found a positive result, then the posterior probability for H 1 {\displaystyle H_{1}} is P r ( H 1 | find H 1 ) = 0.50 {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})=0.50} , and the replication probability is P r ( replication |


id: replication-crisis-20260505-11
file: data/en.wikipedia.org/wiki/Replication_crisis-11.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
== Consequences == When effects are wrongly stated as relevant in the literature, failure to detect this by replication will lead to the canonization of such false facts. A 2021 study found that papers in leading general interest, psychology and economics journals with findings that could not be replicated tend to be cited more over time than reproducible research papers, likely because these results are surprising or interesting. The trend is not affected by publication of failed reproductions, after which only 12% of papers that cite the original research will mention the failed replication. Further, experts are able to predict which studies will be replicable, leading the authors of the 2021 study, Marta Serra-Garcia and Uri Gneezy, to conclude that experts apply lower standards to interesting results when deciding whether to publish them. === Public awareness and perceptions === Concerns have been expressed within the scientific community that the general public may consider science less credible due to failed replications. Research supporting this concern is sparse, but a nationally representative survey in Germany showed that more than 75% of Germans have not heard of replication failures in science. The study also found that most Germans have positive perceptions of replication


id: replication-crisis-20260505-12
file: data/en.wikipedia.org/wiki/Replication_crisis-12.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
In response to concerns in psychology about publication bias and data dredging, more than 140 psychology journals have adopted result-blind peer review. In this approach, studies are accepted not on the basis of their findings and after the studies are completed, but before they are conducted and on the basis of the methodological rigor of their experimental designs, and the theoretical justifications for their statistical analysis techniques before data collection or analysis is done. Early analysis of this procedure has estimated that 61% of result-blind studies have led to null results, in contrast to an estimated 5% to 20% in earlier research. In addition, large-scale collaborations between researchers working in multiple labs in different countries that regularly make their data openly available for different researchers to assess have become much more common in psychology. ==== Pre-registration of studies ==== Scientific publishing has begun using pre-registration reports to address the replication crisis. The registered report format requires authors to submit a description of the study methods and analyses prior to data collection. Once the method and analysis plan is vetted through peer-review, publication of the findings is provisionally guaranteed, based on whether the authors follow the proposed protocol. One goal of


id: replication-crisis-20260505-13
file: data/en.wikipedia.org/wiki/Replication_crisis-13.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Cross-validation ==== One common statistical problem is overfitting, that is, when researchers fit a regression model over a large number of variables but a small number of data points. For example, a typical fMRI study of emotion, personality, and social cognition has fewer than 100 subjects, but each subject has 10,000 voxels. The study would fit a sparse linear regression model that uses the voxels to predict a variable of interest, such as self-reported stress. But the study would then report on the p-value of the model on the same data it was fitted to. The standard approach in statistics, where data is split into a training and a validation set, is resisted because test subjects are expensive to acquire. One possible solution is cross-validation, which allows model validation while also allowing the whole dataset to be used for model-fitting. === Replication efforts === ==== Funding ==== In July 2016, the Netherlands Organisation for Scientific Research made €3 million available for replication studies. The funding is for replication based on reanalysis of existing data and replication by collecting and analysing new data. Funding is available in the areas of social sciences, health research and healthcare innovation. In 2013, the


id: replication-crisis-20260505-14
file: data/en.wikipedia.org/wiki/Replication_crisis-14.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:42:46.886632+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The dominant scientific and statistical model of causation is the linear model. The linear model assumes that mental variables are stable properties which are independent of each other. In other words, these variables are not expected to influence each other. Instead, the model assumes that the variables will have an independent, linear effect on observable outcomes. Social scientists Sebastian Wallot and Damian Kelty-Stephen argue that the linear model is not always appropriate. An alternative is the complex system model which assumes that mental variables are interdependent. These variables are not assumed to be stable, rather they will interact and adapt to each specific context. They argue that the complex system model is often more appropriate in psychology, and that the use of the linear model when the complex system model is more appropriate will result in failed replications. ...psychology may be hoping for replications in the very measurements and under the very conditions where a growing body of psychological evidence explicitly discourages predicting replication. Failures to replicate may be plainly baked into the potentially incomplete, but broadly sweeping failure of human behavior to conform to the standard of independen[ce] ... ==== Replication should seek to revise theories ==== Replication is


id: reproducibility-20260505-0
file: data/en.wikipedia.org/wiki/Reproducibility-0.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:42:48.119894+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge. == History == The first to stress the importance of reproducibility in science was the Anglo-Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum existence. Historians of science Steven Shapin and Simon Schaffer, in their 1985 book Leviathan and the Air-Pump, describe the debate between Boyle and Hobbes, ostensibly over the nature of vacuum, as fundamentally an argument about how useful knowledge should be gained. Boyle, a pioneer of the experimental method,


id: reproducibility-20260505-1
file: data/en.wikipedia.org/wiki/Reproducibility-1.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:42:48.119894+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
=== Reproducible research method === The term reproducible research refers to the idea that scientific results should be documented in such a way that their deduction is fully transparent. This requires a detailed description of the methods used to obtain the data and making the full dataset and the code to calculate the results easily accessible. This is the essential part of open science. To make any research project computationally reproducible, general practice involves all data and files being clearly separated, labelled, and documented. All operations should be fully documented and automated as much as practicable, avoiding manual intervention where feasible. The workflow should be designed as a sequence of smaller steps that are combined so that the intermediate outputs from one step directly feed as inputs into the next step. Version control should be used as it lets the history of the project be easily reviewed and allows for the documenting and tracking of changes in a transparent manner. A basic workflow for reproducible research involves data acquisition, data processing and data analysis. Data acquisition primarily consists of obtaining primary data from a primary source such as surveys, field observations, experimental research, or obtaining data from an existing source.


id: reproducibility-20260505-2
file: data/en.wikipedia.org/wiki/Reproducibility-2.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:42:48.119894+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
== Noteworthy irreproducible results == Hideyo Noguchi became famous for correctly identifying the bacterial agent of syphilis, but also claimed that he could culture this agent in his laboratory. Nobody else has been able to produce this latter result. In March 1989, University of Utah chemists Stanley Pons and Martin Fleischmann reported the production of excess heat that could only be explained by a nuclear process ("cold fusion"). The report was astounding given the simplicity of the equipment: it was essentially an electrolysis cell containing heavy water and a palladium cathode which rapidly absorbed the deuterium produced during electrolysis. The news media reported on the experiments widely, and it was a front-page item on many newspapers around the world (see science by press conference). Over the next several months others tried to replicate the experiment, but were unsuccessful. Nikola Tesla claimed as early as 1899 to have used a high frequency current to light gas-filled lamps from over 25 miles (40 km) away without using wires. In 1904 he built Wardenclyffe Tower on Long Island to demonstrate means to send and receive power without connecting wires. The facility was never fully operational and was not completed due to economic problems,


