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.
6,766
datasets available to search
ShareScore release 0.7.1
Dataset results
6,766 results for “project”
GitRec - Github Project Recommender Systems
<p>This dataset contains the data collected using the Google API for the GHTorrent project and which were applied in the doctoral thesis directed to recommending projects on the GitHub platform</p>
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
UWB-IODA project, Work package 1: IR-UWB optimized pulses
<p>The data files contain optimized UWB waveforms using B-spline functions. The spectral efficiency of each waveform is maximized under the constraint of the spectral mask defined by the FCC/ECC regulation authorities.</p>
Inter- and transdisciplinary projects in FP7 and H2020 (May 2019)
<p>The metadata of interdisciplinary (IDR) and transdisciplinary (TDR) projects conducted under the European Union framework programs (FP7 & Horizon 2020) were collected from the Cordis database (<a href="https://cordis.europa.eu/">https://cordis.europa.eu/</a>). SHAPE-ID research team used periodic data dumps, stored in EU open data portal (<a href="https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects">https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects</a> and <a href="https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects">https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects</a>). </p> <p>The data dump from <strong>May 2019</strong> was used, so the FP7 database is complete while H2020 projects were still being added periodically.</p> <p>CORDIS files were subsequently queried for interdisciplinar* or transdicsiplinar*, matched against title or abstract (“objective”). This procedure allowed for creating two subsets:</p> <p>FP7_projects_May2019_IDR_TDR.csv 1750 FP7 projects. Out of 1699 IDR projects, interdisciplinar* featured in 40 titles and 1679 abstracts. Out of 56 TDR projects transdisciplinar* featured in 2 project titles and 54 abstracts.</p> <p>1912 H2020 projects (as of May 2019). Out of 1837 IDR projects, interdisciplinar* featured in 57 titles and 1820 abstracts. Out of 85 TDR projects transdisciplinar* featured in 2 project titles and 85 abstracts.</p> <p><strong>Description of the files </strong></p> <p>CSV files contain the same fields as CORDIS database data dumps: id, acronym, status, programme, topics, framework Programme, title, startDate, endDate, projectUrl, objective, totalCost, ecMaxContribution, call, fundingScheme, coordinator, coordinatorCountry, participants, participantCountries, subjects.</p> <p>Additional fields:</p> <p>IDR - project features interdiciplinary research (1 = yes, 0 = no)</p> <p>TDR - project features transdiciplinary research (1 = yes, 0 = no)</p> <p>Title_Interdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Objective_interdisciplinar*- frequency of “interdisciplinar*” in the project objective</p> <p>Title_transdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Obj_transdisiplinar* - frequency of “interdisciplinar*” in the project objective</p> <p>Reference data (countries, funding schemes/types of action, subjects (SIC codes)) can be found in this dataset: <a href="https://data.europa.eu/euodp/en/data/dataset/cordisref-data">https://data.europa.eu/euodp/en/data/dataset/cordisref-data</a></p> <p> </p>
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 1
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 3
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
PROGRAMS project. PRM workflows from IDEKO S. Coop. on 2020-09 sample 2
<p>These data is the PRM workflow elaboration of the ones collected from FIDIA machine tool controller during milling operation.</p>
Incident at TRANSALLOYS weatherstation PRÉMA project
<p>An incident report and video files relevant to activities of work package 2 of the PRÉMA project.</p> <p>The incident involved the theft and attempted theft of solar panels for the weather station collecting atmospheric data for the PRÉMA project</p>
FLAME Project, Open Call 3, FC5 Live Trial
<p>This dataset contains the RAW data recorded during the FC5 Live trial, Open Call 3, FLAME project.<br> The dataset contains the following data:<br> • Server-side metrics: log data of all involved nodes from CLMC (CPU, mem used, bandwidth used)<br> • Client-side metrics: Bitrate, latency, and buffering<br> • Users’ feedback: questionnaires (CSV)</p>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
Data Visualization - Final Project - Global Climate Change
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on Global Climate Change. D3 & Google Visualization API is used for all visualization graphs in the webpage.</p>
Homeostatic Project Management
<p>Description of a homeostatic project management approach.</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
RIGHTS UP - Photographs from the RIGHTS UP project (2018-2020) [Visual data]
<p>This visual data was collected as part of the project RIGHTS UP with the objective of exploring the emergence of social movements critical of mass tourism in Venice, Amsterdam and Barcelona. These images include diverse 'protests' against mass tourism in the aforementioned cities, as well as photographs of tourists at these 'travel destinations'. The images were produced with ethical and privacy concerns as a priority. The attached table provides detail on each individual file, with a short description of the image, the city where it was captured, and the date.</p> <p>The unedited images are in .JPG format and all of them were produced with a Nikon D90 camera. The raw files of these images could be requested to the author, when necessary.</p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
Linguistic Atlas Projects - Atlanta Survey Project - Version 1
<p>Director:<br> William A. Kretzschmar, Jr.</p> <p>Status of work:<br> Field work complete in 2003. Direct CD recordings; some full transcriptions have been completed.</p> <p>Areas covered:<br> Fulton and DeKalb counties in the Atlanta, GA, metropolitan area.</p> <p>Subjects:<br> 18 speakers selected by random phone sampling, with later in-person interviews. African American and nonAfrican American subjects, stratified by gender and occupational status.</p> <p>Nature of recordings:<br> Guided conversational interview expected to last one hour, adapted from Western States interview. Fixed format elicitation in which speakers read words from cards. </p> <p>Archives:<br> Original materials are archived in the Special Collections repository of the Library, University of Georgia, Athens, Georgia 30602. For access to original materials, contact the Linguistic Atlas Project office.</p>
Results from the RDM Survey - LEARN project (December 2016)
<p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the first one published at http://doi.org/10.5281/zenodo.61903. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
<p>We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach on three datasets from different languages.</p> <p>The <strong>russian</strong> model.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Continuum Analytics, Inc.<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.12.0<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p> <p>The <strong>english</strong><strong>-combined</strong> model has been trained using the well-known word embeddings dataset based on Google News: GoogleNews-vectors-negative300.bin on EVALution, BLESS, K&H+N, ROOT09 combined. The <strong>english</strong><strong>-</strong><strong>evalution</strong> model is traned on EVALution only.</p> <p>$ python -V; pip show tensorflow numpy scipy scikit-learn gensim | egrep -i '(name|version)'<br> Python 3.5.2 :: Anaconda custom (64-bit)<br> Name: tensorflow<br> Version: 0.12.1<br> Name: numpy<br> Version: 1.11.3<br> Name: scipy<br> Version: 0.18.1<br> Name: scikit-learn<br> Version: 0.18.1<br> Name: gensim<br> Version: 0.13.4.1</p>
Final Results from the RDM Survey - LEARN project (June 2017)
<p> </p> <p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the previous ones published at http://doi.org/10.5281/zenodo.61903 and http://doi.org/10.5281/zenodo.290635. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.