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339 results for “fairness”
ICOPS Workshop Series - FAIR data
<p><strong>This is the sixth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p> <p>In this workshop we had multiple speakers:</p> <ul> <li> <p>Introduction to FAIR and FAIR Phytoliths project results - Emma Karoune - slides in the main presentation</p> </li> <li> <p>FAIR Data principles and Imaging Resources - Jean-Marie Burel - slides attached as pdf.</p> </li> <li> <p>Sobre la nomenclatura fitolitica en Argentina y el uso del ICPN 2.0 - Maria-Gabriela Musaubach - slides in the main presentation</p> </li> </ul> <p>Youtube video of the workshop:</p> <p>Emma Karoune <a href="https://www.youtube.com/watch?v=VsphfgppcWg&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=2&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_1.Emma.Introduction to FAIR and FAIR Phytoliths Project results - YouTube</a></p> <p>Jean-Marie Burel <a href="https://www.youtube.com/watch?v=yNcLApDHxFY&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=1&ab_channel=ICOPSopenphytoliths">FAIR Data workshop_2.Jean Marie. FAIR Principles and imaging resources - YouTube</a></p> <p>Maria-Gabriela Musaubach <a href="https://www.youtube.com/watch?v=UZizjXCvnbM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=4&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_3.Gabi. Sobre la nomenclatura fitolítica en Argentina, el uso del ICPN 2.0. - YouTube</a></p> <p>Round table <a href="https://www.youtube.com/watch?v=--OO8upPhhM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=3&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_4.Round table on the FAIR Data session - YouTube</a></p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
Fair emissions allocations under various global conditions
<h1>Introduction</h1> <p>This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. This data is applied in a preprint of a <a href="https://www.researchsquare.com/article/rs-5023350/v1">scientific article</a> where we explore implications of justice on NDCs and international mitigation finance.</p> <p>The research behind this dataset is still under development and therefore this dataset is not final. Our scientific work is still under revision so the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence these releases of a preliminary version.</p> <h1>Carbon Budget Explorer</h1> <p>We also published this work on a website called the <em>Carbon Budget Explorer</em>: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at <a href="https://www.carbonbudgetexplorer.eu">www.carbonbudgetexplorer.eu</a>. Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will be updated with new data early 2025 (i.e., with the version presented in this data repository).</p> <h1>Data description</h1> <h3>Default (DefaultAllocations.zip and DefaultReductions.zip)</h3> <p>For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The <em>reductions_default_*.csv</em> files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.</p> <h3>Global pathways (GlobalPathways.zip)</h3> <p>Allocating emissions to countries starts with determining global emissions pathways. The files in <em>GlobalPathways.zip</em> contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the <em>emissionspathways_default.csv</em> datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in <em>emissionspathways_all.csv</em>.</p> <h3>Emission allocations (Allocations.zip -> allocations_*.nc)</h3> <p>The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format <em>allocations_region.nc </em>indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as <em>nccreate</em>, <em>ncread</em>), R (e.g. using the <em>ncdf4</em> package) and Python (e.g. using the <em>xarray</em> package).</p> <h3>Input data (Inputdata.zip)</h3> <p>Additional input data coming from third parties, such as population and GDP data, is stored in <em>Inputdata.zip</em>. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.</p> <h3>CO2 budgets</h3> <p>A file has been added in the version 0.3.1, including cumulative CO2 budgets. How they are calculated, is slightly different for each rule (only PC, AP and ECPC are included here), because of the varying nature of these allocation rules. The PC budget is simply the fraction of the remaining carbon budget determined by a country's 2021 population share. The AP budget is computed by adding all positive CO2 allocations according to the AP rule. The ECPC budget is the full-century budget: that is, historical leftover (or debt) plus a country's fair per capita share between 2021-2100. Note that there is not necessarily a one-to-one relation between these budgets and the CO2 part of the allocation files (<em>Allocations.zip</em>). For example, the PC budget uses 2021 population, while the allocation files use year-to-year population numbers (also if they change in the future). We have the ambition to, in next versions, expand this dataset to account for and vary the choices one can make in this regard.</p> <h1>Allocation rules</h1> <p>Below you can find a summarized description of all allocation rules. More detailed information can be found in <a href="https://link.springer.com/article/10.1007/s10584-019-02368-y" target="_blank" rel="noopener noreferrer">Van den Berg et al. (2020)</a>, as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.</p> <ul> <li>The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis. </li> <li>'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see <a href="https://www.tandfonline.com/doi/full/10.1080/14693062.2021.1970504">Rajamani et al. 2021</a>). It is include here for reference only.</li> <li>The 'Per Capita Convergence' (PCC) method starts as 'Grandfathering', but converges over time to a 'Per Capita' basis. An additional important parameter here is the year at which this convergence completes.</li> <li>The 'Per Capita via Budget' (PCB_lin) method is a specific implementation of distributing the total CO2 budget on a per capita basis, and then drawing a linear line from current emissions down to net-zero CO2. A median non-CO2 path is added to end up with a total greenhouse gas emissions line. This is similar to, for example, <a href="https://newclimate.org/resources/publications/what-is-a-fair-emissions-budget-for-the-netherlands">Fekete et al. (2022)</a>.</li> <li>The 'Ability to Pay' (AP) method allocates emissions inversely related to the GDP per capita of countries. Also this method is dependent on the socio-economic scenario.</li> <li>The 'Equal Cumulative Per Capita' (ECPC) method builds on the per-capita convergence method, also accounts for historical responsibility: throughout the convergence period, countries resolve historical 'debt' or 'leftover' from what countries would have emitted if it had emissions according to a per capita share in the past. <em>Note</em>: this method has been significantly revised in version 0.4. In earlier versions, resolving of historical responsibility was only achieved by 2100, postponing most debt.</li> <li>The 'Greenhouse Development Rights' (GDR) method is, in the short run, based on a <a href="https://calculator.climateequityreference.org/" target="_blank" rel="noopener noreferrer">Responsibility-Capability Index</a>, and in the long run based on GDP per capita (similar to 'Ability to Pay').</li> </ul> <h1>Dimensions</h1> <p><em>Table 1 - Data dimensions</em></p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Range</strong></td> <td><strong>Default</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>General</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Time</td> <td>Year</td> <td> <p>Past: 1850-2021</p> <p>Future: 2021-2100 (yearly or 5-year increments)</p> </td> <td>All</td> <td>The historic data reported here ends in 2021, and we start our analysis in 2021. Intentionally, to be able to exactly match historic and future data. The year 2021 is chosen because of limited availability of more recent data sources.</td> </tr> <tr> <td>Region</td> <td>ISO3 code</td> <td> <p>Country-level (ISO3)</p> <p>Country groups (e.g., G20 and Umbrella)</p> <p>World ('EARTH')</p> </td> <td>All</td> <td> </td> </tr> <tr> <td><strong>Global</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Temperature</td> <td>Degrees temperature rise with respect to pre-industrial times</td> <td> <p>1.5 - 2.0 degrees</p> </td> <td>1.6 and 2.0</td> <td>Peak temperature without overshoot</td> </tr> <tr> <td>Climate sensitivity ('Risk' in the data)</td> <td>Risk of exceeding a certain climate target, based on climate sensitivity percentiles.</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50% (for 1.6 degrees) and 33% (for 2.0 degrees)</p> </td> <td> <p>This governs the uncertainty in climate sensitivity. Because there is still uncertainty about the exact numerical response of temperature to CO2, we have to include this. Low-risk (e.g., 0.17) indicates that we assume a high climate sensitivity: for a given amount of greenhouse gas emissions, temperature rises higher. This means that carbon budgets at a given temperature level have to be lower. Vice-versa for high-risk (e.g., 0.83).</p> </td> </tr> <tr> <td>NegEmis</td> <td>Quantiles of 2100 GHG emissions among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50%</p> </td> <td> <p>Even though negative emissions (predominantly in the second-half of the century) are not very relevant for achieving a certain peak temperature, they do alter the second half of global emissions pathways.</p> </td> </tr> <tr> <td>NonCO2red</td> <td>Quantiles of non-CO2 reductions in 2040 with respect to 2020 among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td>50%</td> <td>Non-CO2 reduction varies greatly among mitigation scenarios, but at the same time has a large effect on the remaining carbon budget. Hence, we vary this factor.</td> </tr> <tr> <td>Timing</td> <td>-</td> <td> <p>Immediate or Delayed</p> </td> <td>Immediate</td> <td>The timing of mitigation action up to 2030. Either this starts immediately (2020) or only after 2030. This factor distinguishes mitigation scenarios from which the functional form of the global emissions pathways are constructed.</td> </tr> <tr> <td><strong>Parameters in allocation rules</strong></td> <td> </td> <td> <p> </p> </td> <td> </td> <td> </td> </tr> <tr> <td>Scenario</td> <td>SSP</td> <td> <p>SSP1-5</p> </td> <td>SSP2</td> <td>Shared-Socioeconomic pathway, defining population and GDP data based on a scenario of how to perceive the future world.</td> </tr> <tr> <td>Convergence_year</td> <td>Year</td> <td> <p>2040, 2050, 2080, 2100</p> </td> <td>2050</td> <td>Year of convergence for the per capita convergence and equal-cumulative per capita rules.</td> </tr> <tr> <td>Discount_factor</td> <td>% per year</td> <td> <p>0%, 1.6%, 2%, 2.8%</p> </td> <td>0%</td> <td>Discount factor of historical emissions, counting from the startyear 2021.</td> </tr> <tr> <td>Historical_startyear</td> <td>Year</td> <td> <p>1850, 1950, 1990</p> </td> <td>1990</td> <td>Year from which and on historical emissions are accounted for in the computation of the responsibility of countries.</td> </tr> <tr> <td>Capability_threshold</td> <td>-</td> <td> <p>No, PrTh, Th</p> </td> <td>Th</td> <td>Implicates whether an additional development threshold should be implemented for the computation of the capability of a country to contribute to mitigation. This is used in the calculations of the Greenhouse Development Rights rule. Entries are (1) no development threshold (No), (2) a threshold of \$7500 (Th) or (3) the \$7500 threshold plus additional progressivity factors. For more information, see <a href="https://joss.theoj.org/papers/10.21105/joss.01273">Holz et al. (2019)</a>.</td> </tr> <tr> <td>RCI_weight</td> <td>-</td> <td> <p>Cap, Half, Resp</p> </td> <td>Half</td> <td>Distinguishes how the Responsibility-Capability Index in the Greenhouse Development Rights rule should weight capability (fully = Cap) or responsibility (fully = Resp). 'Half' indicates that both factors should weigh equally.</td> </tr> </tbody> </table> <h1>Input data sources</h1> <p>For most important data sources, aggregated regions (e.g., G20 and the Umbrella group) are not reported in the original data sources below. We did that aggregation ourselves.</p> <ul> <li>Historic population: UN population data</li> <li>Future population: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Future GDP: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Historical emissions: <a href="https://www.nature.com/articles/s41597-023-02041-1">Jones et al. (2023)</a></li> <li>Emissions pathways (shapes): <a href="../records/7197970">Byers, E. et al. AR6 Scenarios Database. (2022)</a></li> <li>NDC data: <a href="https://themasites.pbl.nl/o/climate-ndc-policies-tool/">PBL NDC tool</a></li> <li>Carbon budgets: <a href="https://essd.copernicus.org/articles/15/2295/2023/">Forster et al. (2023)</a></li> <li>Impact of non-CO2 on carbon budgets: <a href="https://www.nature.com/articles/s43247-023-01168-8">Rogelj et al. (2024)</a></li> </ul> <h1>Changelog</h1> <ul> <li>Version 0.4.2: <ul> <li>Fixed export error that resulted in incomplete PCB_lin data.</li> </ul> </li> <li>Version 0.4.1: <ul> <li>Fixed export error that mixed up the columns in DefaultReductions and DefaultAllocation files.</li> </ul> </li> <li>Version 0.4: <ul> <li>Equal-cumulative per capita is significantly revised in terms of temporal allocation. This has large consequences for short-term allocations in most countries, depending on the convergence year. See description above under 'Allocation rules'.</li> <li>Data is now also available for different analysis starting years, gases and including or excluding LULUCF. This is upon request because this would make the repository too large.</li> <li>In the same spirit, a selection has been made on what to include in these datafiles for completeness and clarity, and what to omit to limit file size and computation problems. If you need any specific parameter combination that you cannot find here, feel free to contact us.</li> <li>Improved data on historical population data and baseline emissions</li> <li>Added units in CSV datafiles</li> </ul> </li> <li>Version 0.3.1: <ul> <li>Added CO2 budgets for additional combinations of global targets (no changes in allocation values)</li> </ul> </li> <li>Version 0.3: <ul> <li>Fixed small error in regional aggregation</li> <li>Cumulative CO2 budgets for PC, AP and ECPC are added in a new file</li> <li>Updated global baseline emissions, which affects AP and ECPC</li> </ul> </li> <li>Version 0.2: <ul> <li>Significant update on LULUCF emissions data and historical emissions data by changing to a more up-to-date data source</li> <li>NDC data update (now from the PBL NDC tool)</li> <li>Added the per-capita via budget rule</li> <li>All the above affect emissions allocations, which are therefore also updated</li> </ul> </li> <li>Version 0.1: <ul> <li>First version of the data</li> <li>Published on the Carbon Budget Explorer</li> </ul> </li> </ul> <h1>Contact</h1> <p>We are very open to suggestions of all kinds. Feel free to contact Mark Dekker at <a href="mailto:mark.dekker@pbl.nl?subject=Effort%20Sharing%20Data">this email address </a>or at the contact form on <a href="https://www.pbl.nl/en/about-pbl/employees/mark-dekker">this website</a>.</p>
FAIR Charging Station data package (Normalised)
<p>FAIR and normalised dataset based on the BNetzA charging station data.</p> <p>Original source: <a href="https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/E-Mobilitaet/Ladesaeulenkarte/start.html">BNetzA Ladesaeulenregister (from 01.12.2024)</a></p> <p>Cleaning and annotation scripts: <a href="https://doi.org/10.5281/zenodo.10201060">FAIR Charging station data</a></p> <p>Metadata key reference:<a href="https://github.com/OpenEnergyPlatform/oemetadata/blob/develop/metadata/latest/metadata_key_description.md"> OEMETADATA Key description</a></p> <p>The data can be loaded individually from the csv files or as a whole using <a href="https://github.com/frictionlessdata/frictionless-py">frictionless.py</a>, for example, unzipping and calling:</p> <p> </p> <blockquote> <p>import frictionless as fl</p> </blockquote> <blockquote> <p>package = fl.Package('bnetza_charging_stations_normalised_01_12_2024.json')</p> </blockquote>
Features of a FAIR vocabulary Supplementary docs
<p>Supplementary docs for FAIR vocabulary feature publication</p> <p>Content:</p> <p>ST1: The suitability of OBO principles used as FAIR Vocabulary Features</p> <p>ST2: FAIR Vocabulary Features mapped to FAIR principles and FAIR vocabulary requirements</p> <p>ST3: VersionIRI analysis</p> <p>ST4: RDA data maturity indicators that are not mapped to FAIR Vocabulary Features</p> <p>ST5: FAIR assessment results of Gene ontology</p> <p>ST6: FAIR assessment results of Experimental Factor Ontology</p> <p>ST7: FAIR assessment results of ICD-11</p>
Demo showing what RELIANCE project has achieved on Open Science, FAIR and EOSC
<p>This demo shows what we have achieved on Open Science and FAIR. </p> <p> </p> <p>- Starting from <a href="https://beta.explore.openaire.eu/">OpenAIRE EXPLORE</a>, we search for "Copernicus air quality" and find lots of resources, mostly publications and only 2 software. The reason is that to be "classified" as "Software", we have to add specific metadata when publishing.</p> <p>- The "Software" we found is a "EOSC Jupyter notebook" created by <a href="https://orcid.org/0000-0003-3979-3645">Simone Mantovani</a> with a DOI and additional metadata so that OpenAIRE explore can "associate" it to a specific EOSC service, namely <a href="https://www.egi.eu/services/notebooks/">EGI Notebook</a>. </p> <p>- When we click on "<a href="https://marketplace.eosc-portal.eu/services/egi-notebooks?q=EGI+Notebook">EOSC Service: EGI Notebook</a>", we are re-directed directly to the service that has been used to generate the original scientific results we found in OpenAIRE explore.</p> <p>- Any EOSC service needs to be requested and you have to plave an "order" to get access to it, where you may have to explain why you would like to access this EOSC service. To authenticate to any EOSC service, you can use for instance your <a href="https://orcid.org/">ORCID </a>identifier. if you do not have one, we suggest to register: this is very handy for EOSC services and you keep your ORCID identifier when you move from one institution to another (in addition, your institutional login may not work).</p> <p>- You will get notified by email (check your SPAM folder!) when you got access to an EOSC service.</p> <p>- We login to EGI notebook using ORCID identifier and upload (manually) the jupyter notebook we found in OpenAIRE (following the link e.g. from zenodo (<a href="https://doi.org/10.5281/zenodo.5554786">https://doi.org/10.5281/zenodo.5554786</a>)</p> <p>- The Jupyter notebook uses CAMS European air quality analysis from Copernicus Atmosphere Monitoring Service. The input data is accessible through an external service called the <a href="https://reliance.adamplatform.eu/">ADAM platform</a> (Advanced geospatial Data Management platform). It hosts datacubes (easy and fast access to large amount of data).</p> <p>- We can re-execute the Jupyter notebook but more importatnly we can create derivative work. However, make sure you check the license of the original result you find in OpenAIRE explore: it needs to have a license that allows you to create derivative work. Also make sure the Jupyter notebook is well documented.</p> <p>- We duplicate the Jupyter notebook and customize it. To bring the Open Science aspect from the beginning and not only when publishing the Jupyter Notebook, we need to use storage that can be shared. We use another service called "<a href="https://www.egi.eu/services/datahub/">EGI datahub</a>".</p> <p>- As when collaboratively writing scientific papers, we agree on how to organize the data: we create an "input folder" (containing all the input datasets used in the Jupyter notebook), an "output" folder with all the outputs we generate and a tool folder with the Jupyter notebook.</p> <p>- The new analysis is very similar to the previous one but over a different geographical area (France). </p> <p>- Finally, we create a Research Object that aggrgate all the resources. We use another external service called <a href="https://reliance.rohub.org/">RoHub </a> (Research Object Hub) and create and "executable Research Object" which we hope will be found, accessed and reused!</p> <p> </p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Testing of AgReFed FAIR data Minimum Thresholds and Stretch Targets
<p>This dataset is a testing of the FAIR thresholds for participation in The Australian Research Federation (AgReFed). The participants in the project assessed their data products before and after project works to improve the maturity of their datasets. The technology and information employed to progress the FAIR maturity of the data was recorded here.</p> <p>This data was used in the testing of the Minimum Thresholds and Stretch Targets developed by Box et al. (2019). Box, Paul, Levett, Kerry, Simons, Bruce, & Wong, Megan. (2019). Guidelines for the development of a Data Stewardship and Governance.</p>
FAIR raw data and heat maps of ARAP deposition modeling
<p>FAIR Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431</a>, DOI: <a href="https://doi.org/10.3390/jpm11050431">https://doi.org/10.3390/jpm11050431</a></p> <p>1. pdf/A of deposition heat maps (incl probability values) for 5 different ARAP modes</p> <p>2. xls-formatted file of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>3.-7. rpt-formatted MPPD v3.04 files of deposition raw data sets for 5 different ARAP modes</p> <p>8.-12. csv-formatted files of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes</p> <p>13. pdf/A of deposition heat maps (incl probability values) for 5 different ERAP modes (upon rehydration of ARAPs)</p> <p>14. txt-formatted README file for Hofer et al 2021</p>
Fair Data Awareness Survey - Australia - 2017
<p>This record describes a survey around the awareness of the FAIR data principles, undertaken in Australia in 2017 by ANDS, Nectar and RDS. ANDS (Australian National Data Service), Nectar (National eResearch Collaboration Tools and Resources), and RDS (Research Data Services) are NCRIS facilities. NCRIS is an Australian Federal Government investment in research infrastructure. ANDS(ands.org.au), Nectar(nectar.org.au) and RDS(rds.edu.au) have integrated their work in line with proposals laid out in the NCRIS Roadmap (https://docs.education.gov.au/node/43736), early in 2017.</p> <p>The survey was conducted as a Google Form, and analysed in a 12 page report (see Summary of Full Results - attached). Results of the demographics and quantitative responses are shared attached to this record. The qualitative responses are not shared, for reasons of confidentiality.</p> <p><strong>Background (from Summary Report)</strong></p> <p>ANDS/RDS/Nectar undertook a baseline survey to assess level of awareness around FAIR in the research community at eResearch Australasia conference (Oct 2017) and through an online survey. The online survey was closed a few weeks later on 16.11.17. A list of questions is provided (see Are you FAIR aware? Google Form.pdf). There were 249 responses.</p>
Making Archaeology FAIR[ER]
<p>In April 2018, at the Society for American Archaeology’s 83rd Annual Meeting in Washington, D.C., Sarah Whitcher Kansa, Julian Richards and Willeke Wendrich hosted a forum entitled ‘Making Archaeology Fair’ (see abstract below). The forum responded to a recently published <a href="https://www.nature.com/articles/sdata201618">article by Wilkinson et al.</a> outlining FAIR data principles, centred on avenues for making scientific data findable, accessible, interoperable and reusable. In preparation for this forum, I produced this illustrated graphic. </p>
Four-stages FAIR Roadmap - FAIR "Pyramid"
<p>On the basis of the experience of a community of practitioners, experts, engineers involved in the development of the EPOS Research Infrastructure, now with the ERIC status, involved in the ENVRI cluster and participating to the ENVRI-FAIR initiative, a common approach was observed, which is reflected into the re-organization FAIR principles into a four-stages roadmap which include: a) <em>data</em> stage, b) <em>metadata</em> stage, c) <em>access</em> stage and d) <em>use</em> stage.<br> These stages correspond to the actual conceptual approach driving day-to-day work of RI implementers in the solid Earth domain (EPOS). </p> <p>Data are usually the main business and wealth of scientists and data practitioners in RIs. As a consequence, the first conceptual step relates to data aspects (<em>Stage 1</em>). Once data is properly managed, RIs professional tend to conceptually tackle the challenge of data description and identification, in order to create the premises for data searchability and contextualization (<em>Stage 2</em>). Once data is properly managed, described and contextualized by means of metadata, then RIs practitioners approach the issue of making it accessible to users (<em>Stage 3</em>). In order to include functionalities that go beyond data access, for instance data analysis and processing, FAIR RIs and data stewardship systems should address a <em>fourth stage</em> concerned with services that <em>make use</em> of data (<em>Stage 1</em>) and metadata (<em>Stage 2</em>) FAIRly accessed (<em>Stage 3</em>) and produce new (meta)data as output.</p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
Comparison results of FAIR Evaluation tools
<p>We studied and compared three automated FAIRness evaluation tools namely F-UJI, the FAIR Evaluator, and FAIR Checker examining three aspects: 1) tool characteristics, 2) the evaluation metrics, and 3) metrics tests for three public datasets. We find significant differences in the evaluation results for tested resources, along with differences in the design, implementation, and documentation of the evaluation metrics and platforms.</p> <p>This data is the comparison results we summarized from the study. All results are reported in our manuscript. This data is the supplementary material of the manuscript. </p>
Scores for calculating automated FAIR assessments in the low carbon energy domain
<p>Results for an automated FAIR assessment of 80 databases from the low carbon energy domain. The assessment was performed with the help of the FAIR maturity evaluation service of Wilkinson et al. The FAIR status with respect to 16 FAIR criteria is listed. The scores are defined to be consistent with the FAIR assessment tool of the Australian Research Data Commons. More details can be found in an additional publication on Zenodo as well as in an upcoming publication by Schwanitz et al.</p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-20231-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI 10.1017/S1431927616012654 Fig. 1d by Zirong Peng and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.</p> <p>The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
FAIR Data Practices in Europe infographic (European Research Data Landscape study)
<p>Infographic of the findings on on FAIR data practices in Europe, part of the European Research Data Landscape study.</p>
Algorithmic Fairness Datasets
<p>This dataset presents the description and the references for the datasets used in the fairness literature. We target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them.</p> <p>For over 95% of the surveyed datasets, we identified at least one contact involved in the data curation process or familiar with the dataset, who received a preliminary version of the respective data brief and a request for corrections and additions. Data briefs are meant as short documentation providing essential information on datasets used in fairness research.</p>
Replication package for: Solidarity and Fairness in Times of Crisis
<p>Replication package (code and data) for:</p> <blockquote> <p>Alexander W. Cappelen, Ranveig Falch, Erik Ø. Sørensen and Bertil Tungodden (2021). Solidarity and fairness in times of crisis. Journal of Economic Behavior & Organization 186: 1-11. <a href="https://doi.org/10.1016/j.jebo.2021.03.017">https://doi.org/10.1016/j.jebo.2021.03.017</a></p> </blockquote>
Dataset for "Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model"
<p>Results of random sampling the segment polarity network with the simulator COPASI. These results correspond to Fig. 2 and Table 2 of von Dassow et. al (2000) (doi:10.1038/35018085). The random sampling was carried out with file vonDassow2000_1x4_alt.cps with COPASI version 4.39 selecting the appropriate parameter set named (1-7) and setting the number of repeats in the parameter scan task to the desired number. Full results of sampling are in files prefixed with the row number of Table 2 of von Dassow et. al (2000) and extension .tsv. Results with scores below 0.2 are in corresponding files with the word "-hits" in the filename. Includes also results from a time course simulation of this model using four different simulators (COPASI, Tellurium, Amici, and VCell). Finally also contains a study on multistability carried out by random sampling of parameters and initial conditions (run with COPASI). Markdown file README.md contains more detailed explanation. See also https://github.com/pmendes/models/tree/main/vonDassow2000</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.