Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,373
datasets available to search
ShareScore release 0.9.0
Dataset results
2,373 results for “working”
SBC LTER: Land: Hydrology: Daily precipitation from Santa Barbara County Flood Control (Public Works Department)
These daily total precipitation data represent legacy data obtained by SBC LTER from the the Santa Barbara County Flood Control District for several coastal watersheds draining into the Santa Barbara Channel (California, USA). Starting in 2001, SBC also began archive high-frequency data from County stations with active data loggers which are available in other SBC data packages. These data were collected in accordance with current industry standards. The data are contained in 3 tables, for stations with active data loggers and tipping gauges, active stations with other types of gauges, and inactive stations. Only data collected with tipping gauges and data loggers can be interpolated to to the resolution required by SBC's research objectives, and so only this data table will be updated (annually, at the end of the water year).
Neural Evidence of the Strategic Choice Between Working Memory and Episodic Memory in Prospective Remembering.
Open the record for dataset details and reuse information.
EEG: Visual Working Memory + Cabergoline Challenge
Open the record for dataset details and reuse information.
EEG: Visual Working Memory in Acute TBI
Open the record for dataset details and reuse information.
Öffentlichkeitsarbeit Forschungsdatenmanagement (PR-Work) - 'Awareness' zum Forschungsdatenmanagement
<p>Diese Fotos dienen der 'Awareness' zum FDM. Sie werden für Broschüren im Sommersemester 2020 benutzt, auf Info.screens, auf der Webseite der Stiftung Universität Hildesheim, usw.</p>
The geologic map of Sinus Iridum, and the geologic units in this work
<p>There are three documents here. </p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/The%20geologic%20map%20of%20Sinus%20Irudum.jpg">The geologic map of Sinus Irudum.jpg</a> shows the geologic map we did. </p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/geounits.zip">geounits.zip</a> shows the geologic units in our work.</p> <p><a href="https://zenodo.org/api/files/30c8e89e-0f0f-47eb-a1f8-2461d1994085/Crater%20counting%20files%20and%20the%20results.zip">Crater counting files and the results.zip</a> is the crater counting files and the results in this work.</p>
Polish is quantitatively different on quartzite flakes used on different worked materials [R analysis]
<p>This upload includes the following files related to the R analysis:</p> <p>- Raw data as a CSV table (processing-quartzite-final.csv), i.e. results from the ConfoMap analysis (see <a href="https://doi.org/10.5281/zenodo.3979116">https://doi.org/10.5281/zenodo.3979116</a>)</p> <p>- RStudio project (Quantification quartzite final.Rproj)</p> <p>- R scripts as R Markdown files (*.Rmd)</p> <p>- R scripts knitted to HTML files (*.html)</p> <p>- An R script (RStudioVersion.R) to write the used version of RStudio to a text file (RStudioVersion.txt)</p> <p>- Output from script #1: processing-quartzite-final.Rbin and processing-quartzite-final.xlsx</p> <p>- Output from script #2: processing-quartzite-final_summary-stats.xlsx</p> <p>- Output from script #3: all plots as PDF files.</p> <p>Note that for running the scripts, the raw data files (processing-quartzite-final.csv, .Rbin and .xlsx) should be stored in a "Data" folder within the working directory.<br> Output (processing-quartzite-final_summary-stats.xlsx and PDF plots) were saved into "Summary-stats" and "Plots" folders, respectively.</p> <p>Zenodo does not allow sub-folders, so this folder structure had to be removed.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Polish is quantitatively different on quartzite flakes used on different worked materials [ConfoMap analysis]
<p>Each surface has been processed with two templates:</p> <p>1) Extract two 50x50 µm sub-areas and extract topography layer from each sub-area. Export sub-areas as SUR files. File names start with "A35" or "VSH4".</p> <p>2) Process all extracted sub-areas for quantitative analysis. File names start with "processing-quartzite-final".</p> <p>All ConfoMap templates are saved in MNT format (including all original and processed surfaces, as well as results). Each template has also been exported to a PDF file.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p> <p>Additionally, the results of the second template are collated into "processing-quartzite-final.csv".</p>
IT Skills Survey in three albanian Social Work Faculties
<p>This IT Skills survey is part of the T@SK project and was distributed among students, administrative staff and teachers from the Social Work Faculties of Albanian universities:</p> <ul> <li>Universiteti i Tiranes</li> <li>Universiteti i Shkodres 'Luigj Gurakuqi'</li> <li>Universitei Aleksandër Xhuvan</li> </ul> <p>The fieldcamp was perfomerd in 2019</p>
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 5. — Undated female red deer upper left canine from Abri des Autours. A, general view; B, detail of perforation; C, detail of the crown with remaining traces of enamel (arrow). Scale bars: A, 10 mm; B, C, 1 mm.
FIG. 7 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 7. — Needle made of a suid left fibula, from the Neolithic collective burial at Abri des Autours: A, general view; B, detail of the tip showing a smooth polish associated with fine and short striations; C, detail of the posterior edge near the point, showing fine parallel striations; D, detail of the posterior edge at the midpoint of the tool, showing smooth polish. Scale bars: A, 10 mm; B, C, D, 150 μm.
FIG. 2 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 2. — Plan of Abri des Autours: A, circular depression; B, rubble of old (unpublished) excavations; C, crack in the rock; D, pit under low wall (from Polet & Cauwe 2007).
FIG. 6 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 6. — Pointed rib of a large bovid, from the Neolithic collective burial at Abri des Autours: A, general view (from left to right: internal, centre and external views); B, detail of the tip (internal view) showing some polish; C, detail of the tip (internal view) showing fine perpendicular striations; D, detail of a lateral edge of the rib devoid of striations. Scale bars: A, 10 mm; B, 500 μm; C, D, 100 μm.
FIG. 3 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 3. — Stratigraphy of Abri des Autours, east–west profile (modified from Cauwe 1994): 1a, b, clay, without pebbles; 2, as layer 6, but with more clay; 3a, brown, argillaceous deposit with small, densely dispersed pebbles; 3b, as layer 3a, but less humic. Contains the Neolithic multiple burial; 3c, as layer 3a, but strongly compacted by calcite precipitations; 4, clay with fine pebbles; 5a, b, cryoclastic deposit without humic or clayish fraction; layer 5b corresponds to very fine gravels, while layer 5a includes larger elements. The Mesolithic single burial extends through both of these layers; 6, brown-grey clayey deposit, strongly inclined. Contains the Mesolithic collective burial; 7, cryoclastic layer, included in clayey orange-brown sediment; 8, thin pebble deposit mixed with greyish sediment; 9, low wall constructed of unworked stone blocks.
Author Classifications of O*NET Individual Work Activities (IWA)
<p>Author Classifications of O*NET Individual Work Activities (IWA) used in "Innovations and Economic Output Scale with Social Interactions in the Workforce"</p>
Individual work of Bachelor students monitored using a dynamic assessment dashboard
<p>This document explain how data were generated and how to interpret them. </p> <p><strong>LICENSE: CC0</strong><br> But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license. <br> Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.</p> <p> </p> <p><strong>HOW DATA WERE COLLECTED</strong><br> Data provided in this dataset were collected in Fall 2016 as part of the Bachelor course 'Formation des usagers en bibliothèques' taught at the University of applied sciences Geneva (Information Sciences Department). <br> Data were generated by the interaction of the students with DAD, the Dynamic assessment dashboard, software to be released in 2017 (https://github.com/grolimur/DAD). Only data related to individual activities are published in this set. They were then cleaned and anonymised.</p> <p>The activities.csv file is provided for information purpose.</p> <p>Data are provided in 2 formats:</p> <p>* Comma-spearated values (submissions.csv)<br> * SQLite (submissions.sqlite)</p> <p>CSV is not correctly read by Excel, it's recommended to convert it into an .xslx file before using it. <br> SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).</p> <p> </p> <p><strong>CODEBOOK</strong><br> Here is the name, the meaning and the possible values of the columns (name - description [possible values]).</p> <p>ID - database row ID [(database unique row ID)]<br> ActivityID - unique activity ID [1, 2, 3, 4, 10, 14, 16, 21, 22, 23, 41]<br> StudentID - unique anonymous student ID [#50 to #72]<br> date - recorded by the database [timestamp]<br> year - year extracted from date [2016, 2017]<br> month - month extracted from date [01, 11, 12]<br> day - day extracted from date [1 to 31]<br> weekday - weekday calculated from date [1, 2, 3, 4, 5, 6, 7]<br> hour - hour extracted from date - 00 to 23]<br> minute - minute extracted from date [00 to 59]<br> second - second extracted from date [00 to 59]<br> gr - group activity or not [0]<br> validated - was the submission validated or not [-1 or 1]<br> week - week number [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, H1, H2]</p> <p><em>ActivityID meaning</em><br> Activities are described in activities.csv. This file conatins only individual activities description. <br> The 2 activities iwth the bonus particularity are not present in the data because students didn't need to apply for them. A bonus was automatically added when activity 1 AND 2 AND 3 AND 4 (bonus = activity 5) as well as when activities 21 AND 22 AND 23 (activity 24) were completed.</p> <p><em>Date format</em><br> The date is formatted like YYYY-MM-DD hh:mm:ss.</p> <p><em>Weekday meaning</em><br> 1 stands for Monday, 2 for Tuesday and so on until 7 for Sunday.</p> <p><em>Group meaning</em><br> 0 stands for 'individual activity' and 1 would stand for group activity'. <br> Only entries with gr="0" appears in this dataset.</p> <p><em>Validated meaning</em><br> -1 stands for 'rejected' and 1 for 'validated'.</p> <p><em>Week meaning</em><br> The course lasts 10 weeks. 1 stands for the 1st week and so on until 10 that stands for the 10th week. <br> H1 and H2 stand for the Christmas holiday weeks that took place between week 8 and 9. <br> No submission were sent in weeks 1, 2 and 10.</p> <p> </p>
Forest restoration and fuels reduction work: Different pathways for achieving success in the Sierra Nevada
Fire suppression and past selective logging of large trees have fundamentally changed frequent-fire adapted forests in California. The culmination of these changes produced forests that are vulnerable to catastrophic change by wildfire, drought, and bark beetles, with climate change exacerbating this vulnerability. Management options available to address this problem include mechanical treatments (Mech), prescribed fire (Fire), or combinations of these treatments (Mech + Fire). We quantify changes in forest structure and composition, fuel accumulation, modeled fire behavior, inter-tree competition, and economics from a 20-year forest restoration study in the northern Sierra Nevada. All three active treatments (Fire, Mech, Mech + Fire) produced forest conditions that were much more resistant to wildfire than the untreated control. The treatments that included prescribed fire (Fire, Mech + Fire) produced the lowest surface and duff fuel loads and the lowest modeled fire hazards. Mech produced low fire hazards beginning 7-years after the initial treatment and Mech + Fire had lower tree growth than controls. The only treatment that produced inter-tree competition similar to historical California mixed-conifer forests was Mech + Fire, indicating that stands under this treatment would likely be more resilient to enhanced forest stressors. While Fire reduced modeled fire hazard and reintroduced a fundamental ecosystem process, it was done at a net cost to the landowner. Using Mech that included mastication and commercial thinning resulted in positive revenues and was also relatively strong as an investment in reducing modeled fire hazard. The Mech + Fire treatment represents a compromise between the desire to sustain financial feasibility and the desire to reintroduce fire. One key component to long-term forest conservation will be continued treatments to maintain or improve the conditions from forest restoration. Many Indigenous people speak of 'active stewardship' as one of the key principles in land management and this aligns well with the need for increased restoration in western US forests. If we do not use the knowledge from 20+ years of forest research and the much longer tradition of Indigenous cultural practices and knowledge, frequent-fire forests will continue to be degraded and lost.
Replication package for the paper "A configurational approach to job quality analysis: forms of inequalities at work in Europe"
<p>The following replication package is appended to the article <span><span><span><span>Étienne Penissat</span><span>, </span></span><span><span>Cécile Rodrigues</span><span> & </span></span><span><span>Alexis Spire</span></span></span></span> <span>(2024)</span> "<span>A configurational approach to job quality analysis: forms of inequalities at work in Europe",</span> <span>European Societies,</span> <span>DOI: <a href="https://doi.org/10.1080/14616696.2024.2312950">10.1080/14616696.2024.2312950</a></span></p> <p>The scripts to be run in the following order are:</p> <p>- 1_Penissat_EuropeanSocieties_2023_DataPreparation.R : Recoding, formatting and scope of data used</p> <p>- 2_Penissat_EuropeanSocieties_2023_DataAnalysis.Rmd : Analysis and statistical results</p> <p>The data used in the article comes from the EWCS (2015) - European Working Condition Survey - provided by the European foundation for the improvement of living and working conditions. The data is not available on free access but can be obtained on request. Information on the survey wave used can be found here : https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015</p> <p>- In the first "1_Penissat_EuropeanSocieties_DataPreparation.R" script, the file containing data named "ewcs_1991-2015.dta" is used. The file called "eseg2_trad.csv" contains english labels for the nomenclature of professional positions ESeG. As "ewcs_1991-2015.dta" is not freely available, it is not included in the package and "eseg2_trad.csv" is located in the "data" folder.</p> <p>- The first script creates the data file "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" in the "results" folder.</p> <p>- The second script called "2_Penissat_EuropeanSocieties_DataAnalysis.Rmd" uses the "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" data file and produces the "2_Penissat_EuropeanSocieties_DataAnalysis.html" file containing all code and results presented in the article.</p>
PROCRAFT Final Meeting - Collaborative work between institutions and volunteers by Marie Grima - National Museum of Flight
Open the record for dataset details and reuse information.
FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 in Global access to nomenclatural botanical resources: Evaluating open access availability
FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 and their use of open access (OA), coverage of top 80% of the International Plant Names Index (IPNI) dataset (journal abbreviations follow IPNI, asterisk indicates that the title is included in the Directory of Open Access Journals).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.