# kb

id: aled-edwards-20260505-0
file: data/en.wikipedia.org/wiki/Aled_Edwards-0.md
category: reference
title: Aled Edwards
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Aled_Edwards
date_saved: 2026-05-05T03:49:06.519735+00:00
instance: kb-cron
parent: aled-edwards-20260505-0
summary:
Aled Morgan Edwards (born June 1, 1962) is the founder and Chief Executive of the Structural Genomics Consortium, a charitable public-private partnership. He is Professor of Medical Genetics and Medical Biophysics at the University of Toronto, Visiting Professor of Chemical Biology at the University of Oxford, and adjunct professor at McGill University. == Early life == Born in Holyhead, Wales, Edwards moved to Canada in 1965 with his parents Undeg and Iwan Edwards, a choral conductor who was awarded the Order of Canada in 1995 for his contributions to Canadian music. == Education == Edwards earned his bachelor's degree (1983) and his Ph.D. (1988) in biochemistry from McGill University supervised by Peter Braun. He carried out post-doctoral studies at Stanford University in the laboratory of Roger Kornberg, where he first crystallized RNA polymerase II, a structure for which Kornberg was awarded the Nobel Prize in Chemistry in 2006. == Research contributions == From 1992 to 1997, while a professor at McMaster University, Edwards became interested in developing structural biology methods and was among the first to use mass spectrometry to identify regions of proteins prone to crystallization. He used this technique to facilitate the crystallography of key proteins involved in


id: european-open-science-cloud-20260505-0
file: data/en.wikipedia.org/wiki/European_Open_Science_Cloud-0.md
category: reference
title: European Open Science Cloud
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/European_Open_Science_Cloud
date_saved: 2026-05-05T03:49:07.702753+00:00
instance: kb-cron
parent: european-open-science-cloud-20260505-0
summary:
The European Open Science Cloud (EOSC) is a European Commission initiative aiming at developing an infrastructure providing its users with services promoting open science practices. Besides being open science oriented, the envisaged infrastructure is built by aggregating services provided by several providers following a System of systems approach. Its goal is to establish a federation of infrastructures facilitating effortless access to interoperable research assets and enhanced services spanning geographical boundaries and diverse academic fields. The initiative started in 2015 with the plan that its organizers finish it by 2020. A European Union committee on research endorsed a plan for the cloud's development in May 2018. The European Open Science Cloud officially launched in November 2018, starting to provide access to services via their EOSC Portal. Public meetings about the project have emphasized the ideological motivations for promoting open science. == EOSC Governance == The development of EOSC is governed by three bodies: An executive board tasked to ensure implementation and accountability; a Governance board, working as an institutional group gathering representatives from the Member States and Associated Countries and from the Commission to ensure effective supervision of the EOSC implementation; and a Stakeholder Forum working as the community actively contributing


id: expression-atlas-20260505-0
file: data/en.wikipedia.org/wiki/Expression_Atlas-0.md
category: reference
title: Expression Atlas
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Expression_Atlas
date_saved: 2026-05-05T03:49:08.798435+00:00
instance: kb-cron
parent: expression-atlas-20260505-0
summary:
The Expression Atlas is a database maintained by the European Bioinformatics Institute that provides information on gene expression patterns from RNA-Seq and Microarray studies, and protein expression from Proteomics studies. The Expression Atlas allows searches by gene, splice variant, protein attribute, disease, treatment or organism part (cell types/tissues). Individual genes or gene sets can be searched for. All datasets in Expression Atlas have its metadata manually curated and its data analysed through standardised analysis pipelines. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas: == Baseline Atlas == The Baseline Atlas provides information about which gene products are present (and at what abundance) under "normal" conditions. This component of the Expression Atlas consists of RNA-seq experiments from ArrayExpress repositories. It aims to answer questions such as: Which genes are specifically expressed in kidney? What is the expression pattern for gene SAA4 in normal tissues? == Differential Atlas == The Differential Atlas allows users to identify genes that are up- or down-regulated in different experimental conditions. == See also == Human Protein Atlas The Cancer Genome Atlas == References == == Further reading == == External links == "About Expression Atlas". European Molecular Biology Laboratory


id: fair-data-20260505-0
file: data/en.wikipedia.org/wiki/FAIR_data-0.md
category: reference
title: FAIR data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FAIR_data
date_saved: 2026-05-05T03:49:10.096945+00:00
instance: kb-cron
parent: fair-data-20260505-0
summary:
FAIR data is data which meets the 2016 FAIR principles of findability, accessibility, interoperability, and reusability (FAIR). The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license. == FAIR principles published by GO FAIR == Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata (defined by R1 below) F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Accessible Once the user finds the


id: figshare-20260505-0
file: data/en.wikipedia.org/wiki/Figshare-0.md
category: reference
title: Figshare
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Figshare
date_saved: 2026-05-05T03:49:11.518948+00:00
instance: kb-cron
parent: figshare-20260505-0
summary:
Figshare is an online open access repository where researchers can preserve and share their research outputs, including figures, datasets, images, and videos. It is free to upload content and free to access, in adherence to the principle of open data. Figshare is one of a number of portfolio businesses supported by Digital Science, a subsidiary of Springer Nature. == History == Figshare was launched in January 2011 by Mark Hahnel and has been supported by Digital Science since a January 2012 relaunch. Hahnel first developed the platform as a personal custom solution for the organization and publication of diverse research products generated in support of his PhD in stem cell biology. In January 2013, Figshare announced a partnership with PLOS to integrate Figshare data hosting, access, and visualization with their associated PLOS articles. In September 2013, the service launched a hosted institutional repository service. In December 2013, they announced integration with ImpactStory to support the collection of altmetrics. Figshare also hosts the Reproducibility Collection as a founding member of The Reproducibility Initiative, which acts as an independent and blinded validator for replication of submitted data. Figshare releases 'The State of Open Data' each year to assess the changing academic landscape


id: galaxy-zoo-20260505-0
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-0.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
Galaxy Zoo is a crowdsourced astronomy project which invites people to assist in the morphological classification of large numbers of galaxies. It is an example of citizen science as it enlists the help of members of the public to help in scientific research. There have been 15 versions as of July 2017. Galaxy Zoo is part of the Zooniverse, a group of citizen science projects. An outcome of the project is to better determine the different aspects of objects and to separate them into classifications. == Origins == A key factor leading to the creation of the project was the problem of what has been referred to as data deluge, where research produces vast sets of information to the extent that research teams are not able to analyse and process much of it. Kevin Schawinski, previously an astrophysicist at Oxford University and co-founder of Galaxy Zoo, described the problem that led to Galaxy Zoo's creation when he was set the task of classifying the morphology of more than 900,000 galaxies by eye that had been imaged by the Sloan Digital Sky Survey at the Apache Point Observatory in New Mexico, USA. "I classified 50,000 galaxies myself in a week, it


id: galaxy-zoo-20260505-1
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-1.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
== Volunteers == Galaxy Zoo recruited volunteers to help with the largest galaxy census ever carried out. Opening the project to the general public saved the professional astronomers the task of studying all the galaxies themselves, resulting in classification of a large number of galaxies undertaken in a shorter time than what smaller research teams would be able to do, classifying 900,000 galaxies in months rather than years if done by smaller research teams. Computer programs had been unable to reliably classify galaxies: several groups had attempted to develop image-analysis programs. Kevin Schawinski stated: "The human brain is actually much better than a computer at these pattern recognition tasks." However, volunteers astonished the project's organizers by classifying the entire catalog years ahead of schedule. An online forum was later set up two weeks after the initial start, partially due to a large volume of emails being sent around, to the point that it was troublesome for those receiving them to process and respond to them. This led volunteers to point out anomalies that on closer inspection have turned out to be new astronomical objects such as 'Hanny's Voorwerp' and 'the Green Pea galaxies'. "I'm incredibly impressed by what they've managed


id: galaxy-zoo-20260505-2
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-2.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
=== Galaxy Zoo 2 === This consisted of some 250,000 of the brightest galaxies from the Sloan Digital Sky Survey. Galaxy Zoo 2 allowed for a much more detailed classification, by shape and by the intensity or dimness of the galactic core, and with a special section for oddities like mergers or ring galaxies. The sample also contained fewer optical oddities. The project which collected classifications from February 2009 - April 2010 and closed with some 60 million classifications. === Galaxy Zoo mergers === This studied the role of interacting galaxies. Interacting galaxies are galaxies that exhibit a gravitational influence on one another. This influence is exhibited over the course of millions or even billions of years as two or more galaxies pass nearby one another. The near passage of two massive structures can cause the galaxies to be distorted and possibly merge. The Galaxy Zoo Mergers aimed to provide a set of tools that allowed users to randomly sample various sets of simulation parameters in rapid succession by showing 8 simulation outputs at a time. This started in November 2009 and was retired in June 2012. === Galaxy Zoo supernovae === Galaxy Zoo used images partner from the Palomar


id: galaxy-zoo-20260505-3
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-3.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
=== Related === In June 2019, citizen scientists through Galaxy Zoo reported that the usual Hubble classification, particularly concerning spiral galaxies, may not be supported, and may need updating. == Rotation of galaxies == One of the original aims for Galaxy Zoo was to explore which way galaxies rotated. Cosmologist Kate Land stated: "Some people have argued that galaxies are rotating all in agreement with each other, not randomly as we'd expect. We want people to classify the galaxies according to which way they're rotating and I'll be able to go and see if there's anything bizarre going on. If there are any patterns that we're not expecting, it could really turn up some surprises." In Galaxy Zoo 1, volunteers were asked to judge from the SDSS images whether the galaxies were elliptical or spiral and, if spiral, whether they were rotating in a clockwise (Z-wise) or anti-clockwise (S-wise) direction. The rotation, also called the chirality, of galaxies has been examined in several Galaxy Zoo related papers. Among the results a psychological bias was demonstrated. Galaxy Zoo scientists wanted to determine whether spiral galaxies were evenly distributed, or whether an intrinsic property of the universe caused them to rotate one


id: galaxy-zoo-20260505-4
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-4.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
The properties of Galactic Dust have been examined in several Galaxy Zoo papers. The interstellar medium of spiral galaxies is filled by gas and small solid particles called dust grains. Despite constituting only a minor fraction of the galactic mass (between 0.1% and 0.01% for the Milky Way), dust grains have a major role in shaping the appearance of a galaxy. Because of their dimension (typically smaller than a few tenths of a micron), they are very effective in absorbing and scattering the radiation emitted by stars in the ultraviolet, optical and near-infrared. Although the interstellar regions are more devoid of matter than any vacuum artificially created on earth, there is matter in space. These regions have very low densities and consist mainly of gas (99%) and dust. In total, approximately 15% of the visible matter in the Milky Way is composed of interstellar gas and dust. The study of dust in galaxies is interesting for many reasons. For example, the dimming effects of dust need to be corrected for to estimate the total mass of a galaxy from measurements of its light. Standard candles used to measure the expansion history of the Universe also need to be corrected for


id: global-urban-evolution-project-20260505-0
file: data/en.wikipedia.org/wiki/Global_Urban_Evolution_Project-0.md
category: reference
title: Global Urban Evolution Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Global_Urban_Evolution_Project
date_saved: 2026-05-05T03:49:13.929625+00:00
instance: kb-cron
parent: global-urban-evolution-project-20260505-0
summary:
The Global Urban Evolution Project is an international collaborative project which was started by Marc T. J. Johnson at the Centre for Urban Environments of the University of Toronto Mississauga (UTM). It includes partners from at least 5 continents, 26 countries, and 160 cities. As a field study of evolution, and as a global study of the effects of urbanization on evolution, its scale is unprecedented. It has been described as "the best replicated test of parallel evolution, on the largest scale ever attempted". The project uses white clover as a model organism for studying global urbanization and urban evolution. White clover was chosen because it already grew in most cities worldwide. It examines the plant's production of hydrogen cyanide (HCN) in urban and more rural environments ("urban-rural clines"). Hydrogen cyanide deters herbivores and increases clover's tolerance for water stress. The project has demonstrated that urban environments are altering the ways in which plants evolve locally, and that similar changes are occurring globally, a demonstration of parallel evolution. It enables researchers to better understand the nature of urban environments, the adaptive capacity of species, and their ability to deal with rapid global environmental changes. == Project history == In 2018,


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: 2026-05-05T03:49:15.371909+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


