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6,025 results for “Science of science”
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
Recommender Systems for Science: A basic Taxonomy
<p>This dataset is accompanying the "<strong>Recommender system for science: A basic taxonomy</strong>" paper published at IRCDL 2022 conference. </p> <p>This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. </p> <p>This dataset contains <strong>209 papers </strong>of interest that have been published between 2015 and 2022.</p> <p>The dataset has <strong>11</strong> columns which organised as follows: </p> <p>Column <strong>Title: </strong>This column contains the title of the papers.</p> <p>Column <strong>DOI: </strong>This column contains the DOI of the papers.</p> <p>Column <strong>Publication_year</strong>: This column contains the year that the paper is published.</p> <p>Column <strong>DB: </strong>This column contains the repository that the paper is retrieved.</p> <p>Column <strong>Keywords</strong>: This column contains the keywords provided for the paper.</p> <p>Column <strong>Content_type: </strong>This column contains the paper type which can be: <strong>Article,</strong> <strong>Conference</strong> or <strong>Review.</strong></p> <p>Column <strong>Citing_paper_count: </strong>This column contains the citation number of the paper.</p> <p>Column <strong>Recommended_artefact: </strong>This column contains the scientific product that is recommended to users which can be <strong>paper</strong>, <strong>workflow</strong>, <strong>collaborator</strong>, <strong>dataset</strong> or <strong>others</strong>.</p> <p>Column <strong>User_type: </strong>This column contains the type of user who receives the recommendation, which can be an<strong> Individual </strong>user or a<strong> Group</strong> of users.</p> <p>Column <strong>Algorithm</strong><strong>: </strong>This column contains the recommendation algorithm that the paper proposed, which can be: <strong>HB </strong>(Hybrid-based), <strong>CB</strong> (Content-based), <strong>CFB</strong> (Collaborative-filtering-based), or <strong>GB</strong> (Graph-based).</p> <p>Column <strong>Evaluation_method</strong><strong>: </strong>This column contains the method of the algorithm evaluation which can be <strong>OFFLINE</strong>, <strong>ONLINE, BOTH, </strong>or<strong> NO_EVALUATION.</strong></p>
The Surface Biology and Geology Architecture Study Science and Applications Traceability Matrix (SATM)
<p>This dataset provides a synthesis of the National Academies of Sciences 2017 Earth Science Decadal Survey mission most and very important objectives for the Surface Biology and Geology Earth Observing system consisting of a global visible to shortwave infrared imaging spectrometer and a multi-spectral thermal infrared radiometer. These objectives are traced to performance criteria used for assessing hundreds of potential architecture variants in an architecture study outlined in a paper titled: "Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s".</p>
Airline Satisfaction Survey Data: Data Science for Business with Python
<p>Companion dataset for the textbook entitled "Data Science for Business with Python"</p>
Data supporting "Large-scale citizen science programs can support ecological and climate change assessments"
<p>Text file of phenology observations pulled from the USA National Phenology Network's database (www.usanpn.org) and used in this analysis. </p>
Citizen science for traffic counts: WeCount project dataset for inner-city streets of Ljubljana
<p>The Horizon 2020 project WeCount is a citizen science project that involves citizens in all steps from problem definition to data collection and analysis. This is currently one of the most common methods of citizen participation. The ethical criteria that such a project must meet in order to be classified as citizen science, and the form of transparency or informed consent that should be a necessary part of the ethical conduct of citizen science projects, were on Telraam platform for examination at the international and national level during the collection of data on traffic flows for WeCount Ljubljana. Engaged citizens were given low-cost sensors which they placed on the inside of the windowpane in their home or office facing the street at different distances (from 3 to 15 meters). </p>
TOPS Open Science Graphics
<p>Two TOPS figures promoting open science.</p>
Forschungsdaten zur Masterarbeit "Open Science und wissenschaftliche Bibliotheken. Disruptive Potentiale digitaler Transformation am Beispiel der Sächsischen Landesbibliothek — Staats- und Universitätsbibliothek Dresden"
<p><em>--- english version below ---</em></p> <p>Im Rahmen des Fernstudiengangs Bibliotheks- und Informationswissenschaften habe ich die Masterarbeit mit dem Titel "Open Science und wissenschaftliche Bibliotheken. Disruptive Potentiale digitaler Transformation am Beispiel der Sächsischen Landesbibliothek — Staats- und Universitätsbibliothek Dresden" im Zeitraum vom 17.02.2022 bis 17.06.2022 angefertigt. </p> <p>Für die Arbeit wurden Mitarbeitende in Interviews zum Thema "Open Science" befragt. Die Interviews wurden nach der Repertory-Grid-Technik durchgeführt und analysiert nach der Interpretive-Clustering-Methode, die erstmals <a href="http://doi.org/10.1080/14780887.2020.1794088">2020 von Burr, King und Heckmann</a> beschrieben wurde. Unterstützend konnte ein von Mark Heckmann entwickeltes Tool zur IC-Analyse verwendet werden: <a href="http://ic.openrepgrid.org/">http://ic.openrepgrid.org/</a>.</p> <p>Im Vorfeld der Interviews mussten Elemente als Basis gefunden werden. Die Elemente wurden mithilfe einer Korpusanalyse eines SLUB-Textkorpus mit dem Tool <a href="https://www.sketchengine.eu/">Sketch Engine</a> und nachfolgender Kuratierung gefunden. Es handelt sich um Nomina, die - nach logDice gewichtet- die höchste Kollokation zu den Begriffen "offen, Offenheit, offenbar" haben. Gewählt wurden:</p> <ul> <li>Werkstatt</li> <li>Kulturdaten</li> <li>Schnittstelle</li> <li>Zugang</li> <li>Meinungsfreiheit</li> <li>Lizenz</li> <li>Standard</li> <li>Wissenschaft</li> <li>Wissen</li> <li>Austausch</li> <li>Makerspace</li> </ul> <p><strong>Als Forschungsdaten zur Arbeit liegen vor:</strong></p> <ul> <li>Ergebnisse der Kollokationsanalyse zu "offen", "Offenheit" und "offenbar"</li> <li>Interviewleitfaden für die Interviews nach Repertory-Grid-Technik</li> <li>Vorlage für den ersten Teil der Interviews (Konstruktbildung)</li> <li>Vorlage für den zweiten Teil der Interviews (Skaleneinordnung der Elemente)</li> <li>Ergebnisse des ersten Teils der Interviews (Konstruktbildung)</li> <li>Ergebnisse des zweiten Teils der Interviews (Skaleneinordnung der Elemente)</li> <li>Ergebnisse der Analyse der Interviews nach der Interpretive-Clustering-Methode</li> <li>Beispiel für die (ungenügende) automatische Transkription eines Interviewanfangs mithilfe der Google Speech to Text API</li> </ul> <p>-----</p> <p><em>english version</em></p> <p>As part of the distance learning course in Library and Information Science, I wrote the Master's thesis entitled "Open Science and academic libraries. Disruptive potentials of digital transformation using the example of the Saxon State Library - State and University Library Dresden" in the period from 17.02.2022 to 17.06.2022. </p> <p>For the work, employees were questioned in interviews on the topic of "Open Science". The interviews were conducted using the repertory grid technique and analysed using the interpretive clustering method, which was first described by <a href="http://doi.org/10.1080/14780887.2020.1794088">Burr, King and Heckmann in 2020</a>. A tool developed by Mark Heckmann for IC analysis could be used as a support: <a href="http://ic.openrepgrid.org/">http://ic.openrepgrid.org/</a>.<br> In the run-up to the interviews, elements had to be found as a basis. The elements were found with the help of a corpus analysis of an SLUB text corpus with the tool <a href="http://www.sketchengine.eu/">Sketch Engine</a> and subsequent curation. These are nouns which - weighted according to logDice - have the highest collocation to the terms "open, openness, apparent". The following were chosen:</p> <ul> <li>Werkstatt</li> <li>Kulturdaten</li> <li>Schnittstelle</li> <li>Zugang</li> <li>Meinungsfreiheit</li> <li>Lizenz</li> <li>Standard</li> <li>Wissenschaft</li> <li>Wissen</li> <li>Austausch</li> <li>Makerspace</li> </ul> <p><strong>The research data available for the work are:</strong></p> <ul> <li>Results of the collocation analysis on "open", "openness" and "apparently".</li> <li>Interview guide for the interviews according to the repertory grid technique</li> <li>Template for the first part of the interviews (construct formation)</li> <li>Template for the second part of the interviews (scale classification of the elements)</li> <li>Results of the first part of the interviews (construct formation)</li> <li>Results of the second part of the interviews (scale classification of the elements)</li> <li>Results of the analysis of the interviews according to the interpretive clustering method</li> <li>Example of (insufficient) automatic transcription of an interview beginning using the Google Speech to Text API</li> </ul> <p> </p>
Citizen Science Initiatives in Bulgaria
<p>This dataset contains original mapping data developed in the process of the Bulgarian Citizen Science landscape review. In total, 20 Bulgarian Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Science communication: How to tell the story of your scientific work
<p>Have you ever wondered why certain research projects get picked up in the news and not others? Or how some researchers manage to produce science content that goes viral on social media? Sure, part of it is luck, but another part of it is storytelling. By framing your research in a different way, you can increase the chances that your story gets picked up, or that your social media gains a following.</p> <p><a href="https://www.youtube.com/watch?v=aasLG7uOGAg">This webinar </a>will give you the tools to tell stories about your research intentionally, identifying newsworthy stories, who’s your audience, what medium best fits your story, and considering whether you want to pitch your story to journalists, or perhaps use your own media production skills, and posting it to social media. But what platform? We will cover all of this and more in part 1 of our Arctic PASSION seminar! This is part 1 of a series of seminars that Arctic PASSION will be hosting. Arctic PASSION is an EU Horizon 2020-funded project which aims to build a coherent Arctic Observing System that is adjusted to societal needs based on a co-design of knowledge.</p> <p>The Arctic PASSION Online Seminar and Dialogue Series is a tool to communicate project’s topics, share ideas, plans and results, and initiate an inclusive and proactive dialogue with people from different groups, backgrounds and career levels. It is targeted to Arctic and Indigenous Youth, Early Career Scientists and other interested audiences. The online seminar is led by Olivia Rempel, a documentary filmmaker and multimedia journalist working at GRID-Arendal, where she does everything from producing, shooting and editing documentaries, to guest teaching a mini science communication course at the Technical University of Denmark. She holds a master’s degree from the UC Berkeley Graduate School of Journalism, with prior undergraduate work in both journalism and environmental studies. Olivia has had a variety of media jobs, from logistics and communication work at Students on Ice, an educational polar expedition organization, to leading open-source investigations that combat disinformation at the UC Berkeley Human Rights Center and working on documentaries that have been screened at film festivals from Svalbard to Addis Ababa. Olivia has been working alongside passionate researchers for much of her career, and one of her greatest joys is helping them ensure their important work is communicated accurately and effectively.</p> <p>Useful Links:</p> <p>Watch this video on Youtube: <a href="https://www.youtube.com/watch?v=aasLG7uOGAg Olivia's public profile and contact details: https://www.grida.no/staff/108">https://www.youtube.com/watch?v=aasLG7uOGAg </a></p> <p>Olivia's public profile and contact details: <a href="https://www.grida.no/staff/108">https://www.grida.no/staff/108</a></p> <p>Olivia's slides: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbE13RnFpMDZiMlFMSjlOLV9oMVRfWjFrdm5Zd3xBQ3Jtc0ttWlJVZzVPMGE1djNsTGgwcmMweUNpeUkwQVhCT1FSdy1wVEFTRjdlRVhRVW41dkZmODZWbUNLc1VBeVhfNW9lYnRqTTdJOEdhc1hBUzQtV0dvcUpDQjNiYzRUV0NpSDZ3UzRrRl9mSl90c3Z3cW9hVQ&q=https%3A%2F%2Fnextcloud.awi.de%2Fs%2F2Gnj8pprcia9mD6&v=aasLG7uOGAg">https://nextcloud.awi.de/s/2Gnj8pprci...</a></p> <p>List of databases mentioned: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbnVNenZNcEFLV01ielJrdFVQa3hkZlpDMnYyQXxBQ3Jtc0tsNXZOT2h3VHJtNTZ0akVtdzVLNFl3Smd1dmdvN1RVSnB6VXRacHFuSGZFM3hJUGtMOHI3UXpEaXhPWnRoOGZnOFYxYXdoTXdwN2JaSTBLb0Z5bERfczh1VEFiWUFfVDFQaDdBRktPT1I1cDdpVnVLRQ&q=https%3A%2F%2Fresearchguides.journalism.cuny.edu%2Ffindingexperts%2Fdiverse-experts&v=aasLG7uOGAg">https://researchguides.journalism.cun...</a></p> <p>GRID-Arendal media resources, free for reuse: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0V3WVA2bUhHeVp5dUVLQUxRaW5pYU9UUm5jQXxBQ3Jtc0trYXBsUExha2d3QmMydzhDOEtaUjJnZU1DMElrdFE3QXV2Rmo5d0NSRXh6UWdhLTVjdC1XT0E3VkhIQUx0c193cjcwd0w4NEo4cnBFTzhfY19DYXlRR3FJTzd3VWtRZEl6dHFTOWJiVk9jekRYX1c5Yw&q=https%3A%2F%2Fwww.grida.no%2Fresources&v=aasLG7uOGAg">https://www.grida.no/resources</a></p> <p>Science communication citations: Bickford D, Posa MRC, Qie L, Campos-Arceiz A, Kudavidanage EP. Science communication for biodiversity conservation Biological conservation.. 2012 Jul;151(1):74-76. DOI: 10.1016/j.biocon.2011.12.016.</p> <p>Bullock OM, Shulman HC and Huskey R (2021) Narratives are Persuasive Because They are Easier to Understand: Examining Processing Fluency as a Mechanism of Narrative Persuasion. Front. Commun. 6:719615. doi: 10.3389/fcomm.2021.719615</p> <p>Márquez MC and Porras AM (2020) Science Communication in Multiple Languages Is Critical to Its Effectiveness. Front. Commun. <a href="https://www.youtube.com/watch?v=aasLG7uOGAg&t=331s">5:31</a>. doi: 10.3389/fcomm.2020.00031</p> <p>Pavelle S and Wilkinson C (2020) Into the Digital Wild: Utilizing Twitter, Instagram, YouTube, and Facebook for Effective Science and Environmental Communication. Front. Commun. 5:575122. doi: 10.3389/fcomm.2020.575122</p>
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Model output and PTt marker data for van Agtmaal et al., 2022 (in review), Frontiers in Earth Science
<p>Model output for reproduction of key figures in the manuscript van Agtmaal et al. titled "Quantifying continental collision dynamics for Alpine-style orogens" currently under revision in Frontiers in Earth Science</p>
Supplemental data for: Variations in the naming of malondialdehyde (MDA) in PubMed-, Scopus-, and Web of Science-indexed literature
<p>Three scientific databases (PubMed, Scopus, and Web of Science (WoS)) were consulted (July 14, 2022) to assess the frequency of eight nomenclatural forms of malondialdehyde (MDA). Due to the peculiarities of the search interface of the selected databases, PubMed was searched in the Title and Abstract fields (search query example in PubMed: "malone dialdehyde"[Title/Abstract]), Scopus was searched in the Title, Abstract, and Keywords fields (search query example in Scopus: TITLE-ABS-KEY (“malone dialdehyde”)), and WoS Core Collection was searched in the Title, Abstract, Author keywords, and Keywords Plus fields (search query example in WoS Core Collection: TS=(“malone dialdehyde”)). All types of publications for the years 2002-2021 are taken into account.</p>
Supporting material for "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences"
<p>This repository contains data and analysis code associated with the manuscript: L.P. Schwartz, J. Liénard, S. V. David. (2022) "Impact of gender on formation and outcome of formal mentoring relationships in the life sciences." Figures and tables in the manuscript can be produced by running the make_figures.ipynb notebook. Figures have been marked with headings indicating their position in the manuscript (Figure 1, Figure S1, etc.). In addition, the notebook contains code to reproduce regression analyses that are cited in the text but not directly associated with a figure.</p> <p>Data on mentoring relationships derives from Academic Family Tree (AFT, www.academictree.org) and public data sources on funding, publications, and awards. Inclusion criteria, public data sources, and procedures for linking across sources are described in the manuscript. Personal identifiers for researchers have been anonymized, but remain consistent across all data in the repository. In other words, the personal identifier "1" refers to the same person in all dataframes in the repository. But, that person is *not* the same researcher identified as "1" on the public AFT website.</p> <p><strong>Installation</strong></p> <p>Requires Python 3.x. and Pandas. To load required libraries using Anaconda, run:</p> <p>`conda create --name aft -c conda-forge pandas numpy scipy ipython jupyterlab scipy scikit-learn pandas matplotlib numpy statsmodels seaborn pytables`</p> <p><strong>Dataframes</strong></p> <p>Data is stored as a series of Pandas dataframes within HDF5 or CSV files:</p> <p>* cng_tc: The primary dataset used in the analysis. The name is an acronym for "connections" (i.e. training relationships, "cn"), "gender" ("g"), and "trainee count" ("tc"). Each row contains data on the mentor and trainee in one training relationship. See manuscript for inclusion criteria.</p> <p>* mentors: Data on mentors. Each row contains data on one mentor. See manunscript for inclusion criteria.</p> <p>* mentors_grants, mentors_hindex, mentors_locs_ranked: Subset of mentors with data available for funding (mentors_grants), citation (mentors_hindex), and institution rank (mentors_locs_ranked).</p> <p>* mentors_nobel, mentors_hhmi, mentors_nas: Subsets of mentors that received a Nobel (mentors_nobel), Howard Hughes Medical Institute grants (mentors_hhmi), or membership in the National Academy of Sciences (mentors_nas). See manuscript for details of data sources and linking procedures.</p> <p>* cn, cng, first_names, gn, gn_all, locs: Partial data (connections only, inferred gender only, connections and gender only, location only, first names and inferred gender only) for more inclusive sets of researchers in AFT. They are generally not used used for analysis, but have been included here to calculate statistics on the total amount of data included and to screen for data from U.S. locations.</p> <p>* nsf_gender_phds, nsf_gender_pds: National Science Foundation survey data on gender and fraction PhDs conferred per year (nsf_gender_phds) or fraction postdocs employed per year (nsf_gender_pds). See manuscript for details of data source.</p> <p>* photo: Data for validation of gender inference method.</p> <p><strong>Dataframe columns</strong></p> <p>* amount: Mentor's total funding<br> * amount_adj: Mentor's total funding (adjusted to 2020 dollars)<br> * broad_field: Mentor's general research area (e.g., life sciences, engineering, based on National Science Foundation classifications)<br> * continue: Whether trainee went on to become a mentor (i.e., has trainees listed in AFT)<br> * country: Country in which mentor's current institution is located<br> * firstname: First name of researcher (table of first names is not aligned with tables containing anonymized personal identifiers)<br> * first_grant_year: Year of mentor's first grant<br> * funding_rate: Mentor's annual funding rate (since first grant)<br> * funding_rate_adj: Mentor's annual funding rate (since first grant) adjusted to 2020 dollars<br> * hhmi: Whether mentor was granted HHMI funding<br> * hindex: Mentor's hindex<br> * location: Name of mentor's current institution<br> * locid: Identifier for mentor's institution<br> * locid_rank: Postion of mentor's institution in 2015 Quacquarelli-Symonds rankings (lower numbers are better)<br> * locid_rank_rev: Reversed version of "locid_rank" (i.e., higher numbers are better)<br> * majorarea: Mentor's specific research area (e.g, neuroscience)<br> * male_mentor, male trainee: Whether the probability that a researcher's first name is used by a person identifying as a man meets threshold (see manuscript for details on gender inference using first names)<br> * match_score: Score for string match between institution or name of awardee and researcher<br> * mentor_career_start: The date at which the mentor's academic career began<br> * mentor_continue_rate: Fraction of mentor's trainees that become mentors<br> * mentor_continue_rate_ft: Fraction of mentor's woman trainees that become mentors<br> * mentor_continue_rate_mt: Fraction of mentor's man trainees that become mentors<br> * mentor_t_p_male0: Fraction of mentor's trainees that are men<br> * mentor_t_p_male0_gs: Fraction of mentor's trainees that are men (graduate students only)<br> * mentor_t_p_male0_pd: Fraction of mentor's trainees that are men (postdocs only)<br> * mentor_tcount0: Mentor's total number of trainees<br> * nas: Whether mentor is a member of the National Academy of Sciences<br> * nobel: Whether mentor is a Nobel laureate<br> * p_male_mentor, p_male_trainee: Probability that a researcher's first name is used by a person identifying as a man<br> * pid: Anonymized identifier of researcher<br> * pid_mentor: Anonymized identifier of mentor in training relationship<br> * pid_trainee: Anonymized identifier of trainee in training relationship<br> * pq: "1" if data on training relationship is drawn from ProQuest database and has not been manually edited a human AFT user<br> * relation: Type of training relationship (1: graduate student, 2: postdoc)<br> * scorer1, scorer2, scorer3: Results of photo validation of gender inference for each scorer<br> * start: Training start year<br> * stop: Training end year<br> * trainee_tcount: Total people that the trainee has trained<br> * triad: Whether trainee has participated in both a graduate-level and postdoctoral training relationship</p> <p>The cn dataframe follows slightly different naming conventions, but is not generally used in the analysis (pid1 = pid_trainee, pid2 = pid_mentor, startdate = start, stopdate = stop).</p>
Citizen Science Initiatives in Germany
<p>This dataset contains original mapping data developed in the process of the Germany Citizen Science landscape review. In total, 29 German Citizen Science projects were analysed according to a common framework. During the mapping process, we weren’t always able to find information on things that interested us e.g. project impact, stakeholder engagement, the size of the volunteering force. But just because we couldn’t find something does not mean the results or activities did not happen. It goes without saying that absence of evidence is not evidence of absence. Failure to find some information on our part can be explained by the fact that we worked primarily with internet sources, so we had to make do with whatever publicly available information we could find within reasonable time.</p>
Surveying the Open Science Knowledge in a Southern Brazilian University - Survey Questions and Answers
<p>Surveying the Open Science Knowledge in a Southern Brazilian University - Survey Questions and Answers</p>
Illustrations from the Environmental Data Science Book: Shared under CC-BY 4.0 for reuse
<p>Illustrations as part of the <em>Environmental Data Science</em> book.</p> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <blockquote> <p>This illustration is created by Scriberia with The Turing Way community. Used under a CC-BY 4.0 licence. DOI: <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a></p> </blockquote> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <p>You can cite all versions by using the DOI <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a>. This DOI represents all versions, and will always resolve to the latest one.</p> <p><em>This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the Environment & Sustainability theme within that grant & The Alan Turing Institute.</em></p>
Social Sciences Word Embeddings in FastText
<p>These social science word embeddings in FastText have been created from 37,604 open access social science research papers from the social science access repository (https://www.gesis.org/ssoar/home). They are available in German and English.</p> <p>(skipgram model, n-grams with n≥3 and n≤6, different dimensions (100, 150, 200, 300, 500), five epochs, learning rate 0.05, five negative examples)</p> <p>Please cite:</p> <p>Schiffers, Ricardo, Dagmar Kern, and Daniel Hienert. 2022. "Evaluation of Word Embeddings for the Social Sciences." In <em>Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</em>, edited by Stefania Degaetano, Anna Kazantseva, Nils Reiter, and Stan Szpakowicz, 1-6. Gyeongju: Association for Computational Linguistics. <a href="https://aclanthology.org/2022.latechclfl-1.1">https://aclanthology.org/2022.latechclfl-1.1</a>.</p>
Science through ML: Post-storm Cooling Data and Programs
<p>These programs and data are associated with the AGU Space Weather Journal article "Science through Machine Learning: Quantification of Post-storm Thermospheric Cooling".</p>
A Survey of Electron Conics at Jupiter Utilizing the JADE-E Data During Science Orbits 01, 03-30
<p>This dataset provides Figure 1 from the AGU <em>JGR: Space Physics</em> article of the same name as a PNG image. It also includes text files with the data to reproduce Figures 2-13 in the same AGU <em>JGR:Space Physics</em> article.</p> <p><strong>Key Points:</strong></p> <ol> <li>We surveyed the JADE-E data for science orbits 01, 03-30 and found upward, downward, and bidirectional electron conics 2.5% of the time</li> <li>We observed all electron conics to occur most often at altitudes of 0.3-0.4 R<sub>J</sub> and local times of 15-16h</li> <li>We observed all electron conic types to have energies greater than 0.7 keV below an altitude of 0.5 R<sub>J</sub> and over the main auroral region</li> </ol> <p><strong>Abstract</strong></p> <p>We present a survey of electron conics over Jupiter’s high latitude regions utilizing 22.6 hours of data from the Jovian Auroral Distribution Experiment electron (JADE-E) instrument aboard NASA’s Juno spacecraft during science orbits 01 and 03-30. We observed electron conics for about 2.5% of this time and characterized them into three types based on their direction of motion along Jupiter’s magnetic field lines: upward, downward, and bidirectional. We observed the upward electron conics most often and at energies of 0.057-80.1 keV, while we observed the downward electron conics least often and at energies of 0.073-1.2 keV. We observed bidirectional electron conics mostly around the same times and places as the upward electron conics having energies of 0.081-49.6 keV. We observed all electron conic types to occur mostly at altitudes 0.3-0.4 R<sub>J</sub> and local times 15-16h. Furthermore, we observed all electron conic types to have energies greater than 0.7 keV below an altitude of 0.5 R<sub>J</sub> and over the main auroral region.</p> <p> </p>
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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.