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28,427 results for “group”
MIG-seq and molphometric datasets of the Gnamptogenys taivanensis group
<p>MIG-seq and molphometric dasatsets of the Gnamptogenys taivanensis group used in Nguyen et al. (2020).<br>Some errors in two csv files were corrected in this version at 2020-11-26.</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
Dataset: Finite groups of symplectic birational transformations of IHS manifolds of OG10 type
<p>Dataset for the finite symplectic birational actions on IHS manifolds of OG10 type.</p> <p>Contains the data related to the paper, a list of Jupyter notebooks for demonstrating some of the computations, two extra tables recording information about the entries of the dataset, and a file containing a list of exceptional lattices (in the wording of the paper). </p> <p>Read the README.md for more information.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Research Data Alliance Interest Group Professionalising Data Stewardship Career Tracks Survey Dataset
<p>This is the final dataset resulting from the data steward Career Tracks survey that the Reseach Data Alliance (RDA) Interest Group Professionalising Data Stewardship carried out in 2022. Data stewards were defined as professionals who aim at guaranteeing that data is appropriately treated in all stages of the research cycle (i.e., design, collection, processing, analysis, preservation, data sharing and reuse); we invited responses from participants who either now or in the past carried out data stewardship functions, regardless of their job title. The survey asked respondents about their job titles, the organizational context in which they work(ed) including contract types and domains, their educational background, and how they perceive their professional future. </p><p>This dataset publication includes:</p><ol><li>Survey response data in CSV format. The file includes data from 241 respondents who consented to participate in the survey and share the data via a repsoitory, who indicated that they either currently work or have worked in the past in a data stewardship role, and who responded to at least one further question.</li><li>Thematic analysis of the qualitative questions Q11 and Q12 in PDF format.</li></ol>
Number of bovine animals tested by age group, reporting country and target group, 2022
<p>The tables contain the number of bovine animals tested by age group, reporting country and target group, 2020.</p>
Messages posted on 25 pro-Bolsonaro Telegram groups during August 2022
<p>This dataset comprises 195,567 message IDs shared by 6,802 unique Telegram users on 25 pro-Bolsonaro public Telegram groups during August 2022, when the presidential campaign for the 2022 Brazilian elections started. </p><p>The message ID provided by Telegram API is unique only for the group or channel where it was posted. For this reason, the dataset provides the message ID and the group username (@). </p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the eastern Andean slopes and the biogeographic history of the genus"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Three new species of Torrent Treefrogs (Anura: Hylidae) of the Hyloscirtus bogotensis group from the Amazon foothills and the biogeographic history of the genus"</p> <p>The matrix is in NEXUS format and has 3259 bp and 25 terminals.</p> <p>Partitions are as follows:</p> <div>charset 12S = 1-955;</div> <div>charset ND1_nonCoding1 = 956-1279;</div> <div>charset ND1_Pos1 = 1280-2240\3;</div> <div>charset ND1_Pos2 = 1281-2241\3;</div> <div>charset ND1_Pos3 = 1282-2242\3;</div> <div>charset ND1_nonCoding2 = 2243-2361;</div> <div>charset cmyc_Pos1 = 2362-2779\3;</div> <div>charset cmyc_Pos2 = 2363-2780\3;</div> <div>charset cmyc_Pos3 = 2364-2781\3;</div> <div>charset Rag1_Pos1 = 2782-3415\3;</div> <div>charset Rag1_Pos2 = 2783-3416\3;</div> <div>charset Rag1_Pos3 = 2784-3417\3;</div>
Small Group and Survey Datasets: "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study"
<p>These datasets accompany the paper "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study" by Chad Curtis PhD and Kaytlin Gomez.</p> <p>The first dataset consists of free responses from n=12 students researchers during a Small Group Metacognitive Practice (SGMP) intervention conducted during the 2023 INBRE Summer Undergraduate Research Fellowship (iSURF) at Nevada State University. The files include:</p> <ul> <li><strong>transcription.docx</strong>: Transcriptions of both individual (n=12) and small group (n=4) responses from the SGMP session.</li> <li><strong>codingR1.xlsx</strong>: Codings used for the thematic analysis, as coded by the primary investigator.</li> <li><strong>codingR2.xlsx: </strong>Codings used for the thematic analysis, as coded by the co-author.</li> <li><strong>KrippendorffAlpha.xlsx</strong>: Inter-coder reliability calculations using Krippendorff's alpha.</li> </ul> <p>The survey data was collected from n=11 participants at the end of the summer research program. The files include:</p> <ul> <li><strong>surveyData.csv</strong>: Anonymized responses to survey questions regarding the SGMP intervention.</li> </ul> <p>This study was approved by the Nevada State University Review Board (Protocol #2305-0346). All procedures in the study were conducted in accordance with approved protocols. Written informed consent was obtained from the subjects for their anonymized information to be published with the article.</p>
Co-design – Part 3: Focus group with professionals, early-adopters, and late/non-adopters to better detail each intervention designed, who would be involved, and how it would happen
<h3>Description</h3> <p>This qualitative dataset is the <strong>third part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a <strong>online focus group</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The meeting was conducted at Microsoft Teams with the support of an visual board on Miro. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from focus group</h3> <ul> <li><strong>P3_FG-PAN-TRANSCR_R00.docx </strong>(transcription of focus group audio recordings)</li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Student discourse in small-group collaborative contexts in real-world higher education
<p>This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings.</p> <p>There are 28 sessions, composed of 10799 utterances.</p> <p>The columns consist of 13 columns:</p> <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> <p>This annotated dataset was used in the following paper to model challenge moments.</p> <p>For more details on how the dataset was generated, please refer to the paper.</p> <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. <em>Proceedings of the 14th Learning Analytics and Knowledge Conference</em>, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
New Andrews-Curtis trivializations for Miller-Schupp groups presentations. Supplementary materials.
<p>This folder contains supplementary materials for the papers<br>"New Andrews-Curtis trivializations for Miller-Schupp groups presentations" and</p> <p>"Automated Theorem Proving Reveals a Lengthy Andrews-Curtis<br>Trivialization for a Miller-Schupp Trivial Group Presentation" (tentative, as of january 2025) </p>
Sunspot Group Database of the Specola Solare Ticinese
<p><strong>Content:</strong></p> <p>The database contains data for each sunspot group observed and recorded at the Specola Solare Ticinese from 01.06.1957 until 31.12.2024. Recorded for each group are the date and time of observation, code for the observer, the sky quality during observation, the incremental number, the weighted count, the unweighted count (if available), the Zurich classification, the latitude and longitude determined with Digisun (a free software developed by SILSO for measuring and recording sunspot groups features) or, if these are not available, the latitude and longitude determined manually with the Stonyhurst solar disk, the ETHZ signature, and the DOI code of the drawing. </p> <p> </p> <p><strong>Context:</strong></p> <p>The daily sunspot drawings are performed at Specola Solare Ticinese since 1957. Between 1957 and 1980 Specola was the southern observing station belonging to the Eidgenössische Sternwarte of ETH Zurich (director prof. Max Waldmeier). Since 1981 Specola is operated independently by the local Associazione Specola Solare Ticinese. The corresponding sunspot drawings up to 2019 are archived at the ETH-Zurich University Archives with codes HS1304.5 (1957-1980) and HS1304.6 (1981-2019). They are also available in digital format on the platform e-manuscripta ( https://www.e-manuscripta.ch/). The sunspot drawings made after 2020 are archived at Specola Solare Ticinese and the digital version is made available on the site https://sunspots.irsol.usi.ch/.</p> <p>The main purpose of the drawings is to determine the relative number (R) of the solar activity (called also <em>Wolf number </em>or <em>Sunspot Number</em>). In this context the Specola is considered as the pilot station by the world data center SILSO (Sunspot Index and Long-term Solar Observations) in Bruxelles, whose main goal is the production, preservation and dissemination of the international sunspot number. The relative number is calculated according to the formula R = k.(10.g+f) introduced by Rudolf Wolf, where g is the number of sunspot groups, f the number of sunspots and k is a reduction factor which depends from the observer, the instrument and the observing conditions. </p> <p> </p> <p>The drawings are always performed with the white light projection method (diameter of the Sun image 250 mm) according to the Zurich standard. A Zeiss Coudé refractor is used (D/f 150/2250 mm baffled at 8 cm). The drawings are oriented with a precision of ±1° with respect to the heliographic axis. When the seeing is good enough (most of the cases), the quality of the sunspot drawings details is accurate and can be used for the morphological analysis of sunspot groups and for the determination of their heliographic position. </p> <p> </p> <p>The obtained image is specular: heliographic North above and eastern limb on the right. </p> <p> </p> <p>For more information: </p> <p>https://www.specola.ch/en/sunspot-drawings-of-the-specola-solare-ticinese/sunspot-drawings-of-the-specola-solare-ticinese</p> <p>All observations can be found here:</p> <p>https://sunspots.irsol.usi.ch/</p> <p> </p> <p><strong>Columns explanation:</strong> </p> <p> </p> <ul> <li>date </li> </ul> <p>Observation date </p> <p> </p> <ul> <li>time </li> </ul> <p>Observation time (UT) </p> <p> </p> <ul> <li>observer </li> </ul> <p>Observer identifier, according to following list </p> <p> </p> <p>Sergio Cortesi: 1 </p> <p>Edy Alge: 2 </p> <p>Elena Altoni: 3 </p> <p>Michele Bianda: 4 </p> <p>Araldo Pittini: 5 </p> <p>Andrea Manna: 6 </p> <p>Renzo Ramelli: 7 </p> <p>Marco Cagnotti: 8 </p> <p>Anna Cairati: 9 </p> <p>Roberto Cortesi: 10 </p> <p>Michele DeLorenzi: 11 </p> <p>Marco Cortesi: 12 </p> <p>J. Jzsák: 13 </p> <p>Rolf Schmid: 14 </p> <p> </p> <ul> <li>seeing (SIDC) </li> </ul> <p>Quality of the sky during the observation (5=best, 1=very bad) </p> <p> </p> <ul> <li>group nr </li> </ul> <p>Incremental sunspot group number (starting from the beginning of the year) </p> <p> </p> <ul> <li>spot count </li> </ul> <p>Unweighed sunspot counting </p> <p> </p> <ul> <li>weighted spot count </li> </ul> <p>Weighted sunspot counting following the Waldmeier's rule </p> <p>Cortesi, S., Cagnotti, M., Bianda, M. et al. Sunspot Observations and Counting at Specola Solare Ticinese in Locarno Since 1957. Sol Phys 291, 3075–3080 (2016). https://doi.org/10.1007/s11207-016-0872-7 </p> <p>https://link.springer.com/article/10.1007/s11207-016-0872-7 </p> <p> </p> <ul> <li>type </li> </ul> <p>Zurich classification of the sunspots group </p> <p> </p> <ul> <li>latitude </li> </ul> <p>Heliographic group floating point precision latitude (±1°) determined with DigiSun software </p> <p> </p> <ul> <li>longitude </li> </ul> <p>Heliographic group floating point longitude (±1°) determined with DigiSun software </p> <p> </p> <ul> <li>manual latitude </li> </ul> <p>Heliographic group integer latitude (±1°) detetermined manually with Stonyhurst solar disk </p> <p> </p> <ul> <li>manual longitude </li> </ul> <p>Heliographic group integer longitude (±1°) with Stonyhurst solar disk </p> <p> </p> <ul> <li>ETHZ signature </li> </ul> <p>Drawing-specific signature of the ETH Zurich University Archive </p> <p> </p> <ul> <li>URL</li> </ul> <p>URL of the digitized sunspot drawing file. </p> <p> </p> <p> </p>
From the Horse's Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents
<p>Word frequency pairs for courses A, B, C from: </p> <p>Brett A. Becker, Daniel Gallagher, Paul Denny, James Prather, Colleen Gostomski, Kelli Norris, and Garrett Powell. 2022. From the Horse’s Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022), March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3478431.3499392</p> <p><strong>When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do not cite this dataset directly without citing the article.</strong></p>
A refined method for studying foraging behaviour and body mass in group-housed European starlings.
<p>Datasets and R script corresponding to the following manuscript:</p> <p>A refined method for studying foraging behaviour and body mass in group-housed European starlings.</p> <p>Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (<em>Sturnus vulgaris</em>). We demonstrate rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.<strong> </strong></p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 1: Essays, Finland.
<p><strong>Version 1.1.</strong></p> <p><strong>Updated from </strong>https://zenodo.org/record/5517595</p> <p><strong>Changes: </strong>added .csv copy of the dataset. Clarified the README below, and added name of publishing journal. No other changes.</p> <p>Added a FEDORA project README below.</p> <p> </p> <p><strong>Description of dataset:</strong></p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Students’ technological images of the future: implications for science and technology education, </em>accepted to be published in European Journal of Futures Research.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>385 such excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. The numbering was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A-C).</p> <p>The dataset is intended for providing transparency, but it may also be used for further research. Assistance may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_technological_futures_DATA_Zenodo_csv.csv</p> <p>Students_images_of_technological_futures_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/6397196</em></p>
40Ar/39Ar dating of the Barmur Group (Tjörnes beds), northern Iceland.
<p><sup>40</sup>Ar/<sup>39</sup>Ar radiometric ages and whole-rock major element data from four basaltic lavas that underlie, are intercalated with, and overlie the Barmur Group (Tjörnes beds), northern Iceland.</p>
Public WhatsApp groups from the Brazilian online repositories
<p>This repository contains two gzip files with public WhatsApp groups collected from two Brazilian repositories, they are:</p> <ul> <li><a href="https://gruposdezap.com/">Grupos de Zap</a> - db_grupos_whats.json.gz</li> <li><a href="https://gruposwhats.app/">Grupo de Whats</a> - db_grupos_zap.json.gz</li> </ul> <p>The groups were collected on 01/2022.</p> <p>The files contain five properties: </p> <ul> <li><strong>title</strong>: <em>group title</em></li> <li><strong>description</strong>: <em>description of the group informed by the administrator</em></li> <li><strong>created_date</strong>: <em>creation date when the group was registered in the repository</em></li> <li><strong>num_vizualization</strong><em>: times the group was seen on the site - only for the zap groups repository</em></li> <li><strong>category</strong>: <em>group category</em></li> </ul> <p>If you use this dataset cite your paper, please:</p> <ul> <li><a href="https://doi.org/10.1145/3539637.3557056"><em>"Click Here to Join": A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp</em> </a></li> </ul> <p><em>Daniel Kansaon, Philipe Melo, and Fabrício Benevenuto. 2022. “Click Here to Join”: A Large-Scale Analysis of Topics Discussed by Brazilian Public Groups on WhatsApp. In Brazilian Symposium on Multimedia and Web (WebMedia ’22), November 7–11, 2022, Curitiba, Brazil. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3539637.3557056</em></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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