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6,025 results for “science”
SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science
<p>Table of 301 previously unidentified SuperWASP stellar variables and related characteristics, not including rotators and unknown variables. The variable type has been decided by citizen scientists through the SuperWASP Variable Stars Zooniverse project. The types and periods of each object have been assessed by the authors to correct for mis-classifications; whilst they have been corrected as much as possible, some types periods remain best guesses. All periods have an uncertainty of 0.1%.</p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Artikel- und Buchpublikationen aus der Soziologie: Open Science, Open Access zu Texten, Open Access zu Forschungsdaten, Open Access zu Forschungssoftware, Altmetrics [open version]
<p>Die Daten umfassen Stichproben an je 100 Journalartikeln und Buchpublikationen aus der Soziologie. Ausgwählt wurden Veröffentlichungen aus deutschsprachigen und nicht-deutschsprachigen Ländern.</p> <p>Für alle Artikel der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) ob Forschungsdaten zum Artikel verfügbar waren<br> 6) ob Forschungssoftware zum Artikel verfügbar war<br> 7) wie häufig der Artikel in Google Scholar zitiert wurde<br> 8) wie häufig der Artikel im Web of Science zitiert wurde<br> 9) wie häufig der Artikel in Scopus zitiert wurde<br> 10) wie häufig der Artikel in den Sociological Abstracts zitiert wurde<br> 11) wie viele Mendeley User Counts der Artikel aufwies<br> 12) wie häufig der Artikel getwittert wurde (Datenquelle: Topsy)</p> <p> </p> <p>Für alle Bücher der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) wie häufig das Buch in Google Scholar zitiert wurde<br> 6) wie häufig das Buch im Web of Science zitiert wurde (als Cited-Reference-Analyse)<br> 7) wie häufig das Buch in Scopus zitiert wurde<br> 8) wie häufig das Buch in der International Bibliography of the Social Sciences zitiert wurde<br> 9) wie viele Mendeley User Counts das Buch aufwies<br> 10) wie häufig das Buch getwittert wurde (Datenquelle: Topsy)</p> <p><br> Da die Impact-Informationen unter Copyright-Schutz der Datenbank-Anbieter stehen, können diese nicht frei zugänglich gemacht werden. Es existiert jedoch eine nicht offen verfügbare Version dieser Datensammlung, die Impact-Informationen enthält und die auf Anfrage bereitgestellt werden kann.</p>
Preliminary table of Citizen Science initiatives for monitoring soil health
<p>This matrix is the result of collaborative work for Deliverable 1.1 (WP1; T1.1) of the ECHO project. It facilitated the creation of an overview of the current state of the art in projects, initiatives, or activities that have already involved citizens in monitoring soil health, from both inside and outside the European Union. From this, strategic recommendations for ECHO were derived, ensuring that this project not only makes a valuable contribution to the field of soil health monitoring but also sets a precedent for future citizen science endeavors.</p>
Micro and Macro Open Science Perspective Taxonomy
<h1>Micro and Macro Open Science (OS) Perspective Taxonomy</h1> <h2>Micro OS Perspective</h2> <p>Taxonomy related to terminologies and knowledge around the practice (workflow) of the OS knowledge generator (e.g., researcher), based on reading by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> and <a title="UNESCO Recommendation on Open Science" href="https://unesdoc.unesco.org/ark:/48223/pf0000379949" target="_blank" rel="noopener">UNESCO (2021)</a>.</p> <h2>Macro OS Perspective</h2> <p>Taxonomy related to the conceptual ramifications of OS concerning (public) policies, infrastructure, open involvement of social actors (society) and open dialogue with other knowledge systems, based on reading by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> and <a title="UNESCO Recommendation on Open Science" href="https://unesdoc.unesco.org/ark:/48223/pf0000379949" target="_blank" rel="noopener">UNESCO (2021)</a>.</p> <h3>Instructions:</h3> <ul> <li>There is nothing new in this repository. This is just the taxonomy revised and expanded by <a title="Taxonomia da Ciência Aberta: revisada e ampliada" href="https://doi.org/10.5007/1518-2924.2023.e91712" target="_blank" rel="noopener">Silveira et al. (2023)</a> segregated into two perspectives: Micro and Macro;</li> <li>The reason for this segregation was to document the terminologies and knowledge surrounding the practice (workflow) of OS, which from an individual (micro) point of view, is most interest to the researcher;</li> <li>These two concepts are presented in the form of a mindmap in English and Portuguese (four .png files);</li> <li>If the user wishes to develop another mindmap, with another theme and/or colors, or another flowchart, there are also eight *.txt files with the markdown and memaid hierarchies.</li> </ul>
Video 1 - Open Science, why do we need it?
<p>Interview with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Eva Maria Méndez, PhD in Library and Information Science; Joaquín Tintoré, Professor of Physical Oceanography; Michael Arentoft, Head of Unit, Open Science, DG R&I, European Commission; and Iryna Kuchma, Open Access Programme Manager for EIFL on the necessity of funding Open Science infrastructure and Open Science in general in order to accelerate the shift towards more openness and higher quality in science.</p> <p>Open Science is an attitude of collaboration, transparency, and equitability. SOCIB is one research organisation which makes research data available, which in turn triggers a new understanding in oceanography and allows for faster responses to societal needs. Funding Open Science means funding higher quality, faster, and more impactful science. Funders can also fund Open Science infrastructure to facilitate the shift to Open Science.</p>
Video 4 - Open Science: equitable access for everyone.
<p>An interview about the necessity of funding Open Science platforms as a means of achieving equitable science with Iryna Kuchma, Open Access Programme Manager for EIFL; Ana María Cetto, Professor of Physics at Universidad Nacional Autónoma de México; Yensi Flores, Postdoctoral researcher at the Cancer Research Centre, University College Cork; and <span>Bregt Saenen, Senior Policy Officer for Open Science at Science Europe</span>.<br><br></p> <p><span>Science is a global enterprise targeting global problems – limiting access to science defeats this purpose. Open Science platforms are necessary for researchers from all countries and organizations to be able to participate in the global scientific effort. Funding bodies can support Open Science platforms as a way of ensuring equitable and trustworthy science. </span></p> <p> </p>
Video 6 - Open Science: science for and with citizens.
<p><span>An interview on Open Science funding with Ignasi Labastida, director of the Office for the Dissemination of Knowledge, University of Barcelona; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Victoria Tsoukala, Policy Officer – Open Science at the European Commissions, DG-Reearch and Innovation; and Sumithra Vellupilai, Senior Research Officer at the Swedish Research Council.</span></p> <p><span>Universities should fund Open Science as a means of sharing knowledge, and as a means of providing tools for all of society to access knowledge. All parts of society should be able to benefit from the knowledge produced through the scientific process. Making openness the norm is a way for including society’s stakeholders in the research process. This level of openness and transparency requires funding infrastructure for sharing articles, data and other research results.</span></p> <p><span> </span></p>
Video 5 - Open Science: why do we need data stewards.
<p><span>An interview on the need of data professionals and Open Science skills with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Jessica Lindvall, Head of Training at SciLifeLab Training Hub; Anne Sophie Fink, Head of Data Management at DeiC (Denmark); and Sally Chambers, Director at DARIAH-EU.</span></p> <p><span>Modern research and technology can require not only large amounts of data, but also good data quality. Ensuring good data quality requires specialized expertise. Data stewards and other data management professionals support researchers with providing and working with good quality data which are also FAIR. Open software, open infrastructures are also important bits in the Open Science puzzle, all of which require funding. The uptake of Open Science depends on widespread Open Science awareness and skills. These require outreach, training and formal education.</span></p>
Video 2 - Open Science to enable collaboration.
<p><span>Interview with Maria Bellantone, PhD in materials science; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Pilar Rica Castro, Senior project officer for Open Access, Spanish Foundation for Science & Technology; and Iryna Kuchma, Open Access Programme Manager for EIFL on the importance of policies supporting Open Science infrastructures as a tool for implementing and promoting Open Science.</span></p> <p><span>Open Science fosters inter- and transdisciplinarity. This requires open data infrastructure and interoperability. Open Science policies can be a tool for changing how research is performed and assessed. The Spanish Foundation for Science and Technology funds such infrastructures at a local level. This provides digital infrastructures and expertise to make it possible to share interoperable data. This interoperability also makes it possible for infrastructures to collaborate. Funders have a responsibility to ensure that the research they fund makes an impact, and Open Science infrastructure increases the impact potential of research.</span></p>
Video 3 - Open Science: a better return on investment.
<p><span>An interview with Roberto Sabatino, Research Engagement Officer at HEAnet, Dublin Ireland; Nadia Tonello, Data Management Manager at the Barcelona Supercomputing Centre; and Eva Mendes, PhD in Library and Information Science on the potential of Open Science to enhance humanity’s ability to respond to crises and to provide a better return on investment.</span></p> <p><span>Science is increasingly collaborative, and this includes sharing data. Funding needs to include data management, sharing, and infrastructure. Increased interoperability in research can help humanity collaboratively face challenges such as climate change. The response to the Covid-19 pandemic was also facilitated by data sharing, showing the positive societal impact and net benefit of Open Science.</span></p>
Citizen science snow measurements
<p>Data set includes citizen science observations of snow collected mainly from Finland and Sweden. Data set is collected in CHARTER project with a simplified protocol which follows the international snow observational standards. Data set includes 47 measurement occasions. The protocol includes background information such as measurement date and time, location, description of surroundings, reindeer pasture type, and visible trampling or digging in snow. Measurements includes snow depth in 1-5 points and definition of ice and crust layers at 1-2 of the points. A hardness hand test is used for layer detection (pushing snow first with fist, then with 4 fingers, 1 finger, pencil and knife blade, until snow is too hard to be pierced). For each ice and crust layer, distance of the layer top and bottom from the ground is measured. In addition, it was optional to measure properties of all layers in snowpack (hardness, grain type and distances from the ground) and the snow water equivalent by using cylindrical tube to extract and weight sample of snow.</p> <p>Data set includes date, time, location, longitude, latitude, air temperature, signs of foraging, description of surroundings, type of reindeer pasture, ground, snow height, description of snow conditions with your own language, layer distances from ground, grain type for layers, hardness for layers, snow water equivalent (tube diameter, snow height, weight), recent rain on snow events, comments and links to photos.</p>
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
Data Science job offers in Euraxess.
<p>It is not always easy to find job opportunities if you are interested in beginning to do research in a certain field. In this sense, having an up-to-date dataset with job offers in your field of interest would simplify this search. This dataset could be generated using web scraping methods.</p> <p>Although the web scraper we built could be applied to every field, in this project we focused in opportunities related with data science (i.e. data scientist, data analyst, data engineer...) published on <a href="https://euraxess.ec.europa.eu/">EURAXESS</a>.</p> <p>The dataset generated with this package contains job offers obtained from EURAXESS. Each row of the dataset contains different job offers and its attributes. In the example table showed below, the dataset was obtained using "Data Scientist" as keyword, but another keywords would result in different datasets. The columns describing the dataset are:</p> <ul> <li>Job Offer Title: Title of the job offer.</li> <li>Researcher Profile: Expected applicant profile/s.</li> <li>Company: Company offering the job.</li> <li>Hours/Week: Weekly working hours.</li> <li>Country: Country where the job is offered.</li> <li>City: City where the job is offered.</li> <li>Where to Apply: Url or email where to apply to the offer.</li> <li>More info: URL where the offer can be located.</li> </ul> <p>Dataset generated by web scraping methods: https://github.com/avicenteg/euraxess_scraping</p>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1</p> <p>This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 11)
<p>This dataset contains the eleventh edition of the <em>Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW)</em>, a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities (<a href="https://www.ecoom.be/en/data-collections/vabb-shw">more information</a>). Publications in the database are used as one of the parameters of the Flemish performance-based research funding system. Only approved publications are included in this dataset.</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>
Aoife Daly & Ian Tyers 2022. The sources of Baltic oak. Journal of Archaeological Science 139, 105550, https://doi.org/10.1016/j.jas.2022.105550
<p>Past trade in oak from the lands to the east and south of the Baltic Sea was extraordinary in that it dominated the Northern European market for specialized oak products for many centuries. Equally extraordinary is the fact that since the 1980s, when a large corpus of dendrochronological data from artworks was demonstrated to represent the material evidence for this past trade, we, until now, have not been able to pinpoint where, in this large region, the trees for this trade grew. Through our analysis we can now present the likely sources of three major timber groups in this material, and significantly, the new chronologies that we make available here, will be the key foundation for dating and identifying provenance of Baltic oak, for tree-ring research long into the future.</p>
Youth definitions in governmental, philosophical and social science publications
<p>This datasets provides extracted meta data of youth definitions found in governmental, philosophical and social science publications. The definitions are classified according to different schemas and linked with publication resources and other meta data.</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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.