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747 results for “Open Data”
10 Women of Digital Humanities: An Analysis of Linked Open Data
<p>This is a spreadsheet of linked open data, including tweets of the 10 female scholars randomly chosen for this project. This is an experimental study, and was prepared for my own learning. Presentation prepared for a Masters seminar in Information Science at uOttawa in Winter 2020. </p> <p>Linked Open Data in the Humanities was taught by Prof. Constance Crompton.</p> <p>10 Women of Digital Humanities: An Analysis of Linked Open Data</p> <p>tags: dh, digital humanities, feminist dh, computational analysis, Voyant, word clouds, vizualization, digital identifiers, open scholarship</p>
ADCP data of ice-covered and open-channel (macro-turbulent) flow, Pulmanki River, 2016-2020
<p>README of ADCP_data_Lotsari_et_al_Water_opened.zip</p> <p><br> The ADCP data was the basis of the following paper:<br> Macro-turbulent flow and its impacts on sediment transport potential of a subarctic river during ice-covered and open-channel conditions <br> Eliisa Lotsari (1, 2), Michael Dietze (3), Maria Kämäri (4), Petteri Alho (2,5), Elina Kasvi (6,2)</p> <p>1 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, FI-80101, Joensuu, Finland. eliisa.lotsari@uef.fi<br> 2 Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Turku, Finland.<br> 3 Section 4.6 Geomorphology, German Research Centre for Geosciences GFZ Potsdam, D-14473 Potsdam, Germany. mdietze@gfz-potsdam.de<br> 4 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland. maria.kamari@ymparisto.fi<br> 5 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430, Masala, Finland. mipeal@utu.fi<br> 6 Turku University of Applied Sciences, Joukahaisenkatu 3, FI-20520, Turku, Finland. elina.kasvi@turkuamk.fi</p> <p>(Note: During the time of data gathering, Maria Kämäri worked at the University of Eastern Finland, and Elina Kasvi at the University of Turku)</p> <p>The data set has been measured with Sontek M9 or S5 sensors, depending on the time step (Table 1).</p> <p>Table 1. The measurement times, their acronyms (applied in the above mentioned publication)<br> and applied sensors. In the acronyms of the measurement times W=winter low flow period, S=spring<br> (snow-melt flood period), A=autumn low flow period. These information are presented also<br> in the Table 1 of the above-mentioned publication.<br> Date Acronym Sensor<br> 17.2.2016 W2016 M9<br> 25.5.2016 S2016 S5 <br> 10.9.2016 A2016 M9<br> 16.2.2017 W2017 M9 <br> 31.5.2017 S2017 M9 <br> 9.9.2017 A2017 M9 <br> 9.2.2018 W2018 M9 <br> 23.5.2018 S2018 S5 <br> 8.9.2018 A2018 S5 <br> 8.2.2019 W2019 M9 <br> 21.5.2019 S2019 M9 <br> 6.2.2020 W2020 M9 </p> <p>RiverSurveyor Live software, and its most recent version, was used each time.<br> The measurements have been done at Pulmanki River (69°55'59.09" N; 28° 2'34.32" E), Northern Finland, during 2016 - 2020. <br> The data is in directories of corresponding measurement times. The measurement<br> locations cs1, cs2, cs3, cs4, csA, csB and csC can be found in the paper (Figs. 1 and 2). <br> The data is in raw Matlab file format, as exported from the RiverSurveyor Live software.<br> The coordinate system is ENU.</p> <p>When used, the referencing to the paper and DOI ( 10.5281/zenodo.3855035 ) are required.</p>
Open data repository, Boehm-Sturm et al., Phenotyping placental oxygenation in Lgals1 deficient mice using 19F MRI
<p>Open data repository of journal article "Phenotyping placental oxygenation in Lgals1 deficient mice using <sup>19</sup>F MRI"</p>
Enhanced Molecular Spin-Photon Coupling at Superconducting Nanoconstrictions. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1021/acsnano.0c03167</a> plus a table with information on how the data were obtained and processed.</p>
Covid19Kerala.info-Data: A collective open dataset of COVID-19 outbreak in the south Indian state of Kerala
<p>Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0). <br> <br> <strong>Schema and data package</strong><br> Datapackage with schema definition is accessible at <a href="https://codd-k.github.io/covid19kerala.info-data/datapackage.json">https://codd-k.github.io/covid19kerala.info-data/datapackage.json</a>. Provided datapackage and schema are based on <a href="https://specs.frictionlessdata.io/data-package/">Frictionless data Data Package specification</a>.</p> <p><strong>Temporal and Spatial Coverage </strong></p> <p>This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak. </p> <p>The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals. </p> <p><strong>Longevity and Provenance </strong></p> <p>The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.</p> <p><strong>Data Stewardship </strong></p> <p>CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset. </p> <p><strong>License </strong></p> <p>This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below. </p> <p><strong>Disclaimer </strong></p> <p>Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur. </p>
Antwerp precipitation, open water streams and sewer system sensor data
<p>This csv dataset includes historical data for the period 2018-2020 from multiple sensors deployed in Antwerp that can help city services to have a clear view on the actual precipitation in different regions, the water level of different water flows as well as the water flows in the sewer system of the city. This data was used in CUTLER (visualized in Antwerp’s dashboard) to assist in the impact modelling of garden streets.</p> <p>The data set contains:</p> <p>- 6 water level sensors: lora.0004A30B00202D0C, lora.0004A30B00204B8B, lora.0004A30B00200BFE, lora.0004A30B0021F1D4, lora.0004A30B002041F6, lora.0004A30B001FC6DF</p> <p>- 4 pluvio meters: lora.0004A30B002025F5, lora.0004A30B00201DCC, lora.0004A30B001FF6F7, lora.0004A30B001FA140<br> <br> - 3 sewer level meters: lora.0004A30B001FD07B, lora.0004A30B0020112D, lora.0004A30B001F9B4B</p>
Data and MATLAB Code for the paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow"
<p>This link includes the data and MATLAB code files for the research paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow" by Zhou, J.W.; Bro, W.M.; Tick*, G.R.; Mofatakari, H.; Li, Y.; and Cheng, L., which has been submitted to the Journal of Fluids Engineering. These files are edited under the GB18030 character set standard.</p>
Ancient Greek Literature for Advanced Data Processing: A Text Fabric Representation of Open Access Texts in TEI XML
<p>This data set contains a full conversion of Greek texts available in the Perseus Digital Library and the Open Greek and Latin Project to the Text Fabric data format. The main advantage of the Text Fabric datatype over the original TEI XML format is that it utilizes a strict separation of text and annotation in a flat data structure. At the same time, it permits multiple distinct formats of the same text as well as an unlimited depth of (embedded) annotations. Because of its flat data structure, it facilitates easy and clean procedures to analyze, transform, and enrich the available data. Many of these processes are very difficult to conduct while departing from the hierarchically organized XML tree representation.</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
Relevance of national policy in the provision of open data on South African higher education sector
<p>Spreadsheet providing breakdown of the national Acts, Bills and Standards relevant to sharing open data on the governance of higher education in South Africa. Data generated as a component of the situational analysis conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.</p>
Supplementary data for a study of Open Access Article Processing Charges - 2014
<p>Article Processing Charges levied by a set of Gold Open Access journals and hybrid journals in 2014, as collected from the publishers' web sites. Supplementary data to 10.2314/CERN/C26P.W9DT</p>
ROARMAP Open Access Policy data
<p>This data is a dump from ROARMAP [http://roarmap.eprints.org/] taken in June 2015.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>A number of fields have been added including country names, repository urls, continent etc.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
ROARMAP Open Access Policy data
<p>This data is a dump from ROARMAP [http://roarmap.eprints.org/] taken on 24th August 2015.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>A number of fields have been added including country names, repository urls, continent etc.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
SNSF Open Access Monitoring: Data Publications 2013-2014
<p>This spreadsheet contains supplemental data to the report: "Open Access to Publications: SNSF monitoring report 2013 - 2015" (http://doi.org/10.5281/zenodo.584131), where it was found that at least 39% of publications out of SNSF funded grants are either Gold or Green Open Access.</p> <p>The initial data is based on the reported publications to the Swiss National Science Foundation via the grant administration system mySNF as of <strong>11. September 2015.</strong> This data is also available as open data on p3.snf.ch. Please be aware, that grant beneficiaries can update the metadata to the reported publications, including the Open Access status anytime during and after the end of a grant.</p> <p>Prior to validation, the actual publication date of publications with “in press/accepted” status was verified insofar as possible, and the details updated (approx. 3'000 publications). For further processing only publications with <strong>publication date 2013-2014</strong> were considered. Publications with the status “in press/accepted” status were excluded. Duplicates, i.e. publications cited as output for more than one project (approx. 2'500) are also filtered out. Where identifiable as such, pure abstracts (e.g. in journal supplements), working papers and pre-prints were also excluded.</p> <p><br> The basic goal was to locate a DOI for the publication and update the metadata. 78% of the 17'420 publications scrutinised have in the meantime been assigned a DOI. Using the DOI and the linked metadata available in CrossRef, unique searches could be performed in additional sources such as DOAJ, Pubmed and Pubmed Central, OpenAIRE (repository aggregator) and ADS (Astrophysics Data System). After the initial search for open access full text in these external sources, a search was made in Google Scholar using the DOI of the remaining publications still to be validated and links to any open access full text were extracted. Where even the Google Scholar search failed to turn up open access full text, the status was set to “closed access”. The OA status of 4'212 publications from the period 2013 to 2014 remained unclear, but it is more likely that they are in closed rather than open access mode.</p>
Data for "Open Access impact on citations: a case study"
<p>This dataset is a list of 347 papers published in 2010 and retrieved from the Web of Science, Scopus and Google Scholar. For each paper, the number of citations and the citation date(s) have been collected. If the full-text is available online, the date of "liberation" and the URL of the file have been retrieved as well. The objective was to assess the impact of Open access on citation rate and more particularly the impact before and after full-text "liberation".</p> <p> </p>
PIR data and EEG scoring for Wellcome Open Research methods paper (Brown et al 2016)
<p>PIR data and EEG-scored sleep in the Wellcome Open Research article:</p> <p>'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'</p> <p> </p> <p>1sensorPIRvsEEGdata.csv - PIR based actigraphy for mice to compare to EEG-scored sleep</p> <p>EEG_4mice10sec.csv - Manually scored sleep from EEG files (.edf) from 10.5281/zenodo.160118</p> <p>blandAltLandD.csv - paired estimates of sleep by PIR and EEG methods (sum of 4 mice over 1 day in 30min bins)</p> <p><br> 1monthPIRsleep.csv - 1 month of activity for for figure 4</p> <p><br> 24mice_activity_LD1week.csv - activity and sleep for 24 wt mice (for hierarchical clustering in figure 4)<br> 24mice_sleep_LD1week.csv </p> <p> </p> <p> </p>
LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
<p><strong>LSD4WSD V2.0</strong></p><p><strong>L</strong>earning <strong>S</strong>AR <strong>D</strong>ataset for <strong>W</strong>et <strong>S</strong>now <strong>D</strong>etection - Full Analysis Version. </p><p>The aim of this dataset is to provide a basis for automatic learning to detect wet snow. It is based on Sentinel-1 SAR GRD satellite images acquired between August 2020 and August 2021 over the French Alps. The new version of this dataset is no longer simply restricted to a classification task, and provides a set of metadata for each sample.</p><p>Modification and improvements of the version 2.0.0 :</p><ul><li><i>Number of massif:</i> add 7 new massif to cover the all Sentinel-1 images (cf `info.pdf`).</li><li><i>Acquisition:</i> add images of the descending pass in addition to those originally used in the ascending pass.</li><li><i>Sample: </i>reduction in the size of the samples considered to 15 by 15 to facilitate evaluation at the central pixel.</li><li><i>Sample: </i>increased density of extracted windows, with a distance of approximately 500 meters between the centers of the windows.</li><li><i>Sample:</i> removal of the pre-processing involving the use of logarithms.</li><li><i>Sample:</i> removal of the pre-processing involving the normalisation.</li><li><i>Labels:</i> new structure for the labels part: dictionary with keys: `topography`, `metadata` and `physics`.</li><li><i>Labels:</i> `physics`: addition of direct information from the CROCUS model for 3 simulations: Liquid Water Content, snow height and minimum snowpack temperature.</li><li><i>Labels:</i> `topography`: information on the slope, altitude and average orientation of the sample.</li><li><i>Labels:</i> `metadata` : information on the date of the sample, the mountain massif and the run (ascending or descending).</li><li><i>Dataset</i>: removal of the train/test split*</li></ul><p>*We leave it up to the user to use the Group Kfold method to validate the models using the alpine massif information.</p><p>Finally, it consists of 2467516 samples of size 15 by 15 by 9. For each sample, the 9 metadata are provided, using in particular the <a href="https://www.umr-cnrm.fr/spip.php?article265&lang=en">Crocus</a> physical model:</p><ul><li>topography:<ul><li>elevation (meters) (average),</li><li>orientation (degrees) (average),</li><li>slope (degrees) (average),</li></ul></li><li>metadata:<ul><li>name of the alpine massif,</li><li>date of acquisition,</li><li>type of acquisition (ascending/descending),</li></ul></li><li>physics<ul><li>Liquid Water Content (km/m2),</li><li>snow height (m),</li><li>minimum snowpack temperature (Celsius degree).</li></ul></li></ul><p>The 9 channels are in the following order:</p><ul><li>Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH in linear,</li><li>Topographical features: altitude, orientation, slope</li><li>Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref**</li></ul><p>** The reference image selected is that of August 9th 2020, as a reference image without snow (cf. <a href="https://ieeexplore.ieee.org/document/842004">Nagler&al</a>)</p><p>An overview of the distribution and a summary of the sample statistics can be found in the file info.pdf.</p><p>The data is stored in .hdf5 format with gzip compression. We provide a python script to read and request the data. The script is dataset_load.py. It is based on the h5py, numpy and pandas libraries. It allows to select a part or the whole dataset using requests on the metadata. The script is documented and can be used as described in the README.md file</p><p>The processing chain is available at the following <a href="https://github.com/Matthieu-Gallet/LSD4WSD-dataset"><strong>Github</strong></a> address.</p><p>The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.</p><p>The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations.</p><p><strong>Erratum :</strong></p><p>In the dataloader file, the name of the "aquisition" column must be added twice, see the correction below.:</p><blockquote><p>dtst_ld = Dataset_loader(path_dataset,shuffle=False,descrp=["date","massif","aquisition","aquisition","elevation","slope","orientation","tmin","hsnow","tel",],) </p></blockquote><p>If you have any comments, questions or suggestions, please contact the authors: </p><ul><li>matthieu.gallet@univ-smb.fr</li><li>fatima.karbou@meteo.fr</li><li>abdourrahmane.atto@univ-smb.fr</li><li>emmanuel.trouve@univ-smb.fr</li></ul>
(Open) Data literacy: Dataset for a systematic review of the literature
<p>This dataset presents data used for a systematic review of the literature.</p><p>We conducted a comprehensive literature review, in which we identified the following: a) the role of data literacy as one of several barriers to use; and b) open data-related activities that foster informal learning by assisting in the development of critical data literacy as a surrogate for citizens' continued engagement with open data. Following the screening and selection of 66 articles through the use of keyword mapping, the articles were coded and subjected to quantitative analysis. On the one hand, our findings demonstrate that inadequate data literacy hinders the utilisation of open data. Conversely, it seems that open data initiatives create pertinent prospects for fostering technical data literacy among the general public, enabling them to comprehend and engage with decision-making processes that are informed by data. However, critical data literacy as a primary catalyst for the strategic and transformative utilisation of open government data receives scant attention. In conclusion, this research has the potential to provide a foundation for interventions that promote open data literacy and lifelong learning.</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon
<p>List of papers used in literature review for "A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon"</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.