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.
603
datasets available to search
ShareScore release 0.9.0
Dataset results
603 results for “Metadata”
Worldwide Soundscapes project metadata and analysis scripts
<p>The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated passive acoustic monitoring meta-datasets (i.e. meta-data collections). This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description. Additionally, R scripts are provided to replicate the analysis published in [placeholder].</p> <p>The overview of all sampling sites and timelines can be found on the corresponding project on <a href="https://ecosound-web.de/ecosound_web/collection/index/106">ecoSound-web</a>, as well as a <a href="https://ecosound-web.de/ecosound_web/collection/show/49">demonstration collection</a> containing selected recordings. The recordings of this collection were annotated and analysed to explore macro-ecological trends.</p> <p>The audio recording criteria justifying inclusion into the meta-database are:</p> <ul> <li>Stationary (no transects, towed sensors or microphones mounted on cars)</li> <li>Passive (unattended, no human disturbance by the recordist)</li> <li>Ambient (no directional microphone or triggered recordings, non-experimental conditions)</li> <li>Spatially and/or temporally replicated (i.e. multiple sites sampled at the same time and/or multiple days - covering the same daytime - sampled at the same site)</li> </ul> <p>The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database. The data shared here only includes validated collections.</p> <p><strong>Changes from version 4.0.0</strong></p> <p>Added link to the published synthesis.</p> <p><strong>Meta-database CSV files</strong></p> <p><strong>collections</strong></p> <ul> <li>collection_id: unique integer, primary key</li> <li>name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.</li> <li>ecoSound-web_link: link of validated meta-collection on ecoSound-web</li> <li>primary_contributors: full names of people deemed corresponding contributors who are responsible for the dataset</li> <li>secondary_contributors: full names of people who are not primary contributors but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses</li> <li>date_added: when the datased was added (YYYY-MM-DD)</li> <li>URL_open_recordings: internet link for openly-available recordings from this collection</li> <li>URL_project: internet link for further information about the corresponding project</li> <li>DOI_publication: Digital Object Identifiers of corresponding publications</li> <li>core_realm_IUCN: The main, core realm of the dataset according to IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/</li> <li>medium: the physical medium the microphone is situated in</li> <li>locality: optional free text about the locality</li> <li>contributor_comments: free-text field for comments by the primary contributors</li> </ul> <p><strong>collections-sites</strong></p> <ul> <li>dataset_ID: primary key of collections table</li> <li>site_ID: primary key of sites table</li> </ul> <p><strong>sites</strong></p> <ul> <li>site_ID: unique integer, primary key</li> <li>site_name: internal name or code of sampling site as used in respective projects</li> <li>latitude_numeric: site's numeric degrees of latitude</li> <li>longitude_numeric: site's numeric degrees of longitude</li> <li>blurred_coordinates: whether latitude and longitude coordinates are inaccurate, boolean. Coordinates may be blurred with random offsets, rounding, snapping, etc. Indicate the blurring method inside the comments field</li> <li>topography_m: vertical position of the microphone relative to the sea level. for sites on land: elevation. For marine sites: depth (negative). in meters. Only indicate if the values were measured by the collaborator.</li> <li>freshwater_depth_m: microphone depth, only used for sites inside freshwater bodies that also have an elevation value above the sea level</li> <li>realm: Ecosystem type: main realm according to IUCN GET https://global-ecosystems.org/</li> <li>biome: Ecosystem type: main biome according to IUCN GET https://global-ecosystems.org/</li> <li>functional_group: Ecosystem type: main functional group according to IUCN GET https://global-ecosystems.org/</li> <li>contributor_comments: free text field for contributor comments</li> <li>GADM_0: Global ADMinistrative Database level 0 classification of terrestrial site or marine site that is within territorial waters. Source: https://gadm.org/download_world.html</li> <li>IHO: International Hydrographic Organization classification of marine site. Source: https://marineregions.org/downloads.php</li> <li>WDPA: World Database on Protected Areas classification of the site. Source: https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</li> </ul> <p><strong>deployments</strong></p> <ul> <li>dataset_ID: primary key of datasets table</li> <li>deployment: identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.</li> <li>subset_site_ID: If the deployment was not done in all the sites of the corresponding collection, site IDs where the deployment was conducted</li> <li>start_date: date of deployment start</li> <li>start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>permanent: whether the deployment is permanent, boolean</li> <li>end_date: date of deployment end (date when last scheduled operation starts)</li> <li>end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.</li> <li>operation_mode: continuous: recording takes place from the deployment start date-time to deployment end date-time.<br>periodical: recording takes place periodically (i.e., with duty cycle) from the deployment start date-time to deployment end date-time.<br>scheduled: recording takes place during scheduled daily time intervals (optionally with duty cycle)</li> <li>duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". empty if no duty cycle is used. For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes</li> <li>operation_start_time_mixed: only for scheduled recordings: start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>operation_duration_minutes: only for scheduled recordings: duration of operation in minutes, if constant</li> <li>operation_end_time_mixed: only for scheduled recordings: end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Only required if durations are variable. Do not use when end times are ambiguous (for instance, if a recording could be 1 hour or 25 hours long because the end is on the next day). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>high_pass_filter_Hz: frequency of the high-pass filter of the recorder if applied, in Hz. Otherwise, write "none". This may be called a "low-cut" filter too.</li> <li>bit_depth: sampling bit depth of the recordings. Often constant for a particular recorder</li> <li>channels: number of recorded audio channels</li> <li>sampling_frequency_kHz: frequency at which the microphone signal was sampled by the recorder (sounds of half that frequency will be recorded)</li> <li>recorder: recorder used for deployment</li> <li>microphone: microphone used for deployment</li> <li>target_taxa: main IUCN animal taxa that were studied with this deployment, using the exact IUCN Red list names (http://www.iucnredlist.org/), separated by commas. Only genera, families, orders, and classes are accepted. Empty if there was no taxonomic focus (i.e., general soundscapes were the study focus).</li> <li>contributor_comments: free text field for contributor comments</li> <li>exact_recordings: whether the deployment data here have been superseded by inserting more exact recording date-time ranges into the meta-collection on ecoSound-web</li> </ul> <p><strong>recordings (partial download from <a href="https://ecosound-web.de/">ecoSound-web</a>)</strong></p> <ul> <li>recording_id: primary key of the recordings table</li> <li>collection_id: ID of the collection the recording belongs to</li> <li>name: name of the recording</li> <li>site_id: site ID the recording belongs to:</li> <li>recorder_id: ID of the recorder used for the recording (internal ecoSound-web code)</li> <li>microphone_id: ID of the microphone used for the recording (internal ecoSound-web code)</li> <li>recording_gain:recording gain applied for amplifying the audio signal, in decibels</li> <li>duty_cycle_recording: fraction of the recording periode when the recorder is actively recording audio</li> <li>duty_cycle_period: period of the duty cycle, i.e., time between the starts of two subsequent recordings</li> <li>note: comments (contains the target taxon)</li> <li>file_date: date of the recording start</li> <li>file_time: local time of the recording start</li> <li>sampling_rate: audio sampling rate in Hz</li> <li>bitdepth: depth in bits for each audio sample</li> <li>channel_num: number of channels</li> <li>duration: duration of the recording in seconds. Note: duty-cycled recordings cover only a proportion of this duration<strong><br></strong></li> </ul> <p><strong>affiliations</strong></p> <ul> <li>affiliation_id: primary key of affiliations table</li> <li>lab_research_group: Laboratory or research group name</li> <li>department_school_institute: department, school, or institute name</li> <li>university_institution: University or institution name</li> <li>street_address: street address</li> <li>region_state_province_city: region, state, province, or city name</li> <li>postal_code: postal code</li> <li>country: country name</li> </ul> <p><strong>primary_contributors</strong></p> <ul> <li>First_name: First, given name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>Last_name: Last, family name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>ORCiD</li> <li>affiliation_IDs: primary keys of the affiliations' table corresponding affiliations, separated by comma</li> <li>first_tier_position: Author position in first-tier</li> <li>publication_agreement: Has contributor explicitly agreed to share her/his meta-data in the collaboration agreement?</li> <li>co_author_first_synthesis: Has contributor confirmed co-authorship intention in the collaboration agreement?</li> </ul> <p>The following columns describe the contributor's role in the project accordint to <a href="https://credit.niso.org/">CRediT</a> taxonomy.</p> <p><strong>Auxiliary files for reproducing analysis</strong></p> <p><strong>R scripts</strong></p> <ul> <li><strong>acoustic analysis.R: </strong>reproduces the result of the soundscape case studies</li> <li><strong>metadata analysis.R:</strong> reproduces the metadata analysis results in the publication</li> </ul> <p><strong>Data from the demonstration collection (download from ecoSound-web)</strong></p> <ul> <li><strong>demo_recordings.csv:</strong> metadata of the recordings, see recordings table</li> <li><strong>demo_sites.csv: </strong>metadata of the sampling locations, see sites table</li> <li><strong>demo_tags.csv: </strong>data describing annotations made in demonstration recordings for the biophony, anthropophony, geophony, and unknown sound sources</li> <li><strong>spectrograms.zip:</strong> contains PNG format spectrograms used in generating Figure 5</li> </ul> <p><strong>Externally sourced data</strong></p> <ul> <li><strong>GET_areas_2.1.1.csv: </strong>raw data obtained from Keith et al. 2023 (https://doi.org/10.5281/zenodo.10081251), then summarized in QGIS to obtain areas per functional group</li> <li><strong>Havlik_sites.csv:</strong> data obtained from Havlik et al. 2022 supplementary material (https://www.frontiersin.org/articles/10.3389/fmars.2022.919418), originally named "Data Sheet 1.CSV"</li> <li><strong>Sugai_sites_updated.csv:</strong> data obtained from Sugai et al. 2019 (https://doi.org/10.1093/biosci/biy147), personal communication with permission</li> <li><strong>taxonomy.csv:</strong> raw data obtained from IUCN Red List for all animal taxa (https://www.iucnredlist.org/)</li> <li><strong>topography_range_latitude.csv:</strong> raw topography from GEBCO sub-ice data (https://www.gebco.net/data_and_products/gridded_bathymetry_data/), summarised by bins of 10 latitudinal rows</li> </ul>
SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City
<p>SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City Authors</p> <ul> <li>Therrien, J-D<sup>1</sup></li> <li>Maere, T.<sup>1</sup></li> <li>Sanchez-Quete, F.<sup>2</sup></li> <li>Tsitouras, A.<sup>2</sup></li> <li>Goitom, E.<sup>3</sup></li> <li>Cloutier, F.<sup>4</sup></li> <li>Dufour, D.<sup>4</sup></li> <li>Proulx, F. <sup>4</sup></li> <li>Nicolaï, N.<sup>1</sup></li> <li>Philippe, R.<sup>1</sup></li> <li>Tohidi, M.<sup>1</sup></li> <li>Dorner, S.<sup>3</sup></li> <li>Frigon, D.<sup>2</sup></li> <li>Vanrolleghem, P.A.<sup>1</sup></li> </ul> <p>Affiliations</p> <ul> <li><sup>1</sup> model<em>EAU</em>, Département de génie civil et de génie des eaux, Université Laval</li> <li><sup>2</sup> Microbial Community Engineering Lab (MiCEL), Department of Civil Engineering, McGill University</li> <li><sup>3</sup> Polytechnique Montréal</li> <li><sup>4</sup> Ville de Québec</li> </ul> <p>General Remarks</p> <p>Wastewater-based surveillance of SARS-CoV-2 virus can detect between 1 and 30 infected individuals per 100,000 (including asymptomatic ones) by analyzing the population's sewage. As such, this method is very attractive since it costs only a fraction of clinical testing (as low as 1%). Human faeces may contain the virus a few days before a person becomes ill. Thus, this approach allows for detection of outbreaks 2-7 days before the increase in reported cases stemming from clinical screening tests (Bibby et al., 2021). Wastewater-based surveillance complements clinical testing by geolocating outbreaks, which may help targeting intensive screening programs. Moreover, it provides a quick indication of whether new public health measures (e.g., masks, social distancing, confinement, and curfew) are effective.</p> <p>Sampling</p> <p>The reported dataset contains open data collected in the province of Québec as part of the SARS-CoV-2 wastewater-based surveillance program <a href="https://www.centreau.ulaval.ca/en/covid/">CentrEau</a>-COVID. Four of the largest cities in the province (Montréal, Laval, Québec City, and Trois-Rivières), as well as the municipalities of four rural regions (Mauricie, Centre-du-Québec, Bas-St-Laurent, and Gaspésie) participated in the program. The entire dataset includes 31 sampling sites covering approximately half the population of the province of Québec (population size of 8.5 million). The timeframe covered by the dataset varies for each site. The earliest surveillance program was launched in March 2020, others followed soon after. Samples were collected using various methods, such as 24h composite samples, grab samples, and passive sampling using variations on the Moore swab method (Schang et al., 2020)</p> <p>Analysis</p> <p>Prior to the analysis of the samples for SARS-CoV-2, physiochemical parameters such as total suspended solids (TSS), turbidity, conductivity, ammonium concentration, and pH were measured. The samples were subsequently concentred by filtration using a MEC filter (0.45 um), followed by total RNA extraction using the Qiagen AllPrep PowerViral DNA/RNA Kit (Qiagen, USA) with some modifications (beta-mercaptoethanol concentration raised to 10% and lysis performed at 55 °C for 30 minutes) (Ahmed et al., 2020). SARS-CoV-2 viral RNA was detected by a one-step RT-qPCR. To assess the RNA recovery rate of the procedure, samples were spiked before extraction with a known concentration of Bovine Respiratory Syncytial Virus (BRSV) using the Zoetis INFORCE 3 vaccine (Zoetis, USA). In addition to SARS-CoV-2, samples were assessed for Pepper Mild Mottle Virus (PMMoV), the daily load of which is hypothesized to represent the fecal load contributions to the samples at a given site and time. PCR conditions and primer used to collect viral data are described in the files <code>primers.md</code> and <code>PCR conditions.md</code>.</p> <p>Compilation</p> <p>The measurements on wastewater samples carried out by the participating laboratories of this study are found in the <code>WWMeasure</code> table. The values provided by municipalities come from laboratories accredited by the Centre d'expertise en analyse environnementale du Québec (CEAEQ), in compliance with the latter's quality assurance protocols. The COVID-19-related public health data found in the <code>CPHD</code> table were collected from the Institut National de Santé Publique du Québec (INSPQ)'s public reports. Wastewater data taken in-situ at the sampling sites (e.g., the flow at pumping stations or water resource recovery facilities (WRRFs)) are found in the <code>SiteMeasure</code> table and were taken by the institutions responsible for managing the sites. All of the data, stemming from multiple sources, were combined into the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM)</a> standard format using the <a href="https://github.com/modelEAU/ODM-Import">ODM-Import python package</a> (see also Structure).</p> <p>Validation</p> <p>Wastewater and sample data were manually assessed for quality by our research collaborators. Data points for which the quality appeared to be uncertain were tagged with the value <code>True</code> in the <code>qualityFlag</code> column. Conversely, data deemed of good quality have a quality flag of <code>False</code>. Data that were not checked have a quality flag of <code>NA</code>. Textual comments describing the issues with the data points in more detail are also included in the dataset using the <code>notes</code> column of the relevant tables. Note that data validation was carried out by the data custodians responsible for each city in the dataset according to available resources. As the project continues and data validation is undertaken on more sections of the dataset, data may be re-analyzed, flagged, or commented as needed. Revisions to the dataset will be reported to the best of our ability.</p> <p>Structure</p> <p>The data contained in this dataset has been structured according to the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM) for Wastewater-Based Surveillance</a>. This model provides a standardized dictionary to collect and share data and metadata stemming from wastewater-based surveillance programs. By convention, it splits all data into 10+ thematic tables with each record representing a unique measurement, i.e., long format. For convenience, the <code>wide</code> folder presents the data found in all the other tables in a wide format, i.e., multiple measurements are aligned by <code>timestamp</code>, with each column representing a different parameter.</p> <p>Acknowledgements</p> <p>The authors would like to acknowledge that this dataset was collected thanks to the financial support of the Fonds de Recherche du Québec, the Molson Foundation, the Trottier Family Foundation, CentrEau and NSERC. The authors would also like to acknowledge the efforts of Douglas Manuel (Ottawa Hospital) and Howard Swerdfeger (Public Health Agency of Canada) for their original idea for the Open Data Model and continued development.</p> <p>References</p> <ol> <li> <p>Ahmed, W., Bertsch, P.M., Bivins, A., Bibby, K., Farkas, K., Gathercole, A., Haramoto, E., Gyawali, P., Korajkic, A., McMinn, B.R., Mueller, J.F., Simpson, S.L., Smith, W.J.M., Symonds, E.M., Thomas, K. v., Verhagen, R., Kitajima, M., 2020. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Science of the Total Environment 739. <a href="https://doi.org/10.1016/j.scitotenv.2020.139960">https://doi.org/10.1016/j.scitotenv.2020.139960</a></p> </li> <li> <p>Bibby, K., Bivins, A., Wu, Z., North, D., 2021. Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Research 202, 117438. <a href="https://doi.org/10.1016/j.watres.2021.117438">https://doi.org/10.1016/j.watres.2021.117438</a></p> </li> <li> <p>Schang, C., Crosbie, N., Nolan, M., Poon, R., Wang, M., Jex, A., Scales, P., Schmidt, J., Thorley, B.R., Henry, R., Kolotelo, P., Langeveld, J., Schilperoort, R., Shi, B., Einsiedel, S., Thomas, M., Black, J., Wilson, S., McCarthy, D.T., 2020. Passive sampling of viruses for wastewater-based epidemiology: a case-study of SARS-CoV-2 [WWW Document]. URL <a href="https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true">https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true</a> (accessed 1.18.21).</p> </li> </ol>
Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016
<p>This is the first release of Terrestrial Ecosystem Research Network (TERN) Metadata Profile of ISO 19115-3:2016 and ISO 19157-2:2016. The profile will be used by TERN data editorial system (SHaRED v4).</p>
Collected metadata for masses in the early Cappella Sistina choirbooks
<p>This dataset contains a collection of metadata about the early Cappella Sistina choirbooks, once belonging to the Papal Chapel. It presents the mass compositions contained in the manuscript sources V-CVbav MS 14, 23, 35, 41, 49, 51, 63, 197, 64 and concordant sources of these.</p> <p>The data has been collected in the year 2015 and hasn't been double-checked by any other person. Even though the data was collected as conscientiously as possible, it is sometimes incomplete and errors are also possible. The data as well as the data structure has been collected mostly in German. References for this metadata collection can be found under <code>./references</code>.</p> <p>This data has been used already in:</p> <ul> <li>Plaksin, Anna Viktoria Katrin: "Modelle zur computergestützten Analyse von Überlieferungen der Mensuralmusik. Emprische Textforschung im Kontext phylogenetischer Verfahren.", Münster, 2021 (Schriften zur Musikwissenschaft aus Münster 27), online: <a href="http://nbn-resolving.de/urn:nbn:de:hbz:6-59029717067">urn:nbn🇩🇪hbz:6-59029717067</a>, DOI: <a href="https://doi.org/10.26083/tuprints-00017211">10.26083/tuprints-00017211</a></li> </ul> <p>The repo for this dataset can be found here: <a href="https://github.com/annplaksin/earlyCappSistMasses">https://github.com/annplaksin/earlyCappSistMasses</a></p> <p>This release contains the slightly updated version that has been used in <a href="https://doi.org/10.26083/tuprints-00017211">10.26083/tuprints-00017211</a>:</p> <ul> <li>SQL dump has been changed from MySQL to SQLite dialect</li> <li>Views have been updated</li> <li>PKs have been updated to ensure database-wide uniqueness (in a very basic way).</li> </ul> <p><strong>Contents</strong></p> <ul> </ul> <p>The SQLite database set is found under <code>./sql</code>. It contains not only the tables but various views for a better overview. For opening and viewing the sql data, tools like e.g. the <a href="https://sqlitebrowser.org/">DB Browser for SQLite</a> can be used.</p> <p>The <code>./csv</code> folder contains csv exports of the tables and views.</p> <p>In <code>./networkData</code> contains the relations of works and sources as bipartite network in <code>.csv</code> format. The edges are available as edge list or as adjacency matrix. The node list contains very basic information necessary for identification. Please note, this is the raw exported data for analysis.</p>
Metadata on EUbOPEN multiplex chemogenomic compound screen, wave 1
<p>This is the metadata about EUbOPEN multiplex chemogenomic compound screen, wave 1. The corresponding image data is found at <a href="https://www.ebi.ac.uk/biostudies/studies/S-BIAD145">https://www.ebi.ac.uk/biostudies/studies/S-BIAD145</a>.</p> <p>To compile the metadata Excel file into filelists, please use the Python scripts at: <a href="https://doi.org/10.5281/zenodo.6325622">https://doi.org/10.5281/zenodo.6325622</a>.</p> <p> </p>
SciKGTeX Scientific Contribution Metadata LaTeX Package User Evaluation Results & Analysis
<p>The responses and measured variables from 26 participants of the first user test of the SciKGTeX package.</p> <p><a href="https://github.com/Christof93/SciKGTeX">https://github.com/Christof93/SciKGTeX</a></p> <p>Also the raw text source for the evaluation tasks and the result analysis notebook with the results saved as tsv file.</p>
Metadata and annotation data for the XSample corpus on German academic language
<p>The XSample corpus war created in the project <em>XSample</em> (https://www.izus.uni-stuttgart.de/fokus/fdm-projekte/xsample/) at Universität Stuttgart in 2021 by Melanie Andresen and Axel Pichler. It contains 135 German academic journal articles, 45 each from the disciplines linguistics, literary studies and philosophy. The texts themselves cannot be made public for copyright reasons. However, metadata and some annotation data are published here.</p> <p><strong>xsample-metadata.csv</strong><br> This file contains metadata on the texts in the corpus, like journal, title, authors, text length, and the URL to the original paper. It also contains two analytical metrics, 'past-ratio' and 'temp-expr-ratio', that are based on the annotations in the other two files. The variable 'past-ratio' expresses the proportion of verbs in past tense relative to all finite verbs in the text. The variable 'temp-expr-ratio' gives the number of temporal expressions per 1000 token.</p> <p><strong>xsample-heidel.csv</strong><br> This file contains all temporal expressions found and classified by the annotation tool <em>HeidelTime</em> (https://github.com/HeidelTime/heideltime, V. 2.2.1, Strötgen & Gertz 2013 ). The variable 'position' expresses the position of the first character of the temporal expression in the text in characters.</p> <p><strong>xsample-sticker2.csv</strong><br> This file contains all finite verbs found and classified by the annotation tool <em>sticker2</em> (https://github.com/stickeritis/sticker2). The variable 'position' expresses the position of the first character of the finite verb in the text in characters.</p> <p><strong>References</strong><br> Strötgen, Jannik & Michael Gertz. 2013. Multilingual and cross-domain temporal tagging. <em>Language Resources and Evaluation</em>. Springer 47(2). 269–298. <a href="https://doi.org/10.1007/s10579-012-9179-y">https://doi.org/10.1007/s10579-012-9179-y</a>.</p> <p> </p>
Raw and aggregated data for the study introduced in the paper "The way we cite: common metadata used across disciplines for defining bibliographic references"
<p>These data have been gathered in the context of a study aiming to investigate citation practices for referencing different types of entities and, in particular, for understanding the most used metadata in bibliographic references. The data are stored in two documents in XLSX format:</p> <ul> <li>file "links-intext-pointers-and-cited-entity-types.xlsx" - it contains information about whether the in-text reference pointers of the various PDF articles of the corpus have specified hypertextual links from the in-text reference pointers to the denoted bibliographic reference, plus information about the types of all the entities cited by each article in the corpus;</li> <li>file "metadata-bibliographic-references.xlsm" - it contains information about the metadata used to identify the various descriptive elements of all the bibliographic references defined in the article of the corpus.</li> </ul> <p>The methodology used to gather all these data is described in:</p> <blockquote> <p>Santos, E. A. d., Peroni, S., Mucheroni, M. L.: Workflow for retrieving all the data of the analysis introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019". (2020), <a href="https://doi.org/10.17504/protocols.io.bbifikbn">https://doi.org/10.17504/protocols.io.bbifikbn</a></p> </blockquote>
Description and quality metadata files for four spatial landmark datasets
<p>The files define the description and quality metadata of the spatial landmark datasets from camptocamp.org defined <a href="https://doi.org/10.5281/zenodo.6480985">here</a>.</p> <p>Two ISO standards ISO 19115-1:2014 and ISO 19157:2013, recommanded by the INSPIRE Directive for the dissemination of spatial data and the reporting of data quality are used.</p>
Polifonia Corpus - Periodicals Module Metadata - Italian Language
<p>We release the Metadata of the Periodicals module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Periodicals Module Metadata - Spanish Language
<p>We release the Metadata of the Periodicals module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Periodicals Module Metadata - German Language
<p>We release the Metadata of the Periodicals module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p>
Polifonia Corpus - Periodicals Module Metadata - French Language
<p>We release the Metadata of the Periodicals module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Periodicals Module Metadata - Dutch Language
<p>We release the Metadata of the Periodicals module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Books Module Metadata - English Language (Full)
<p>We release the Metadata of the Books module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)
<p>The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).</p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
AmelHap Metadata
<p>Curated sample information for drones processed for AmelHap. Details include:</p> <ul> <li>ENA_Run (European Nucleotide Archive accession number)</li> <li>ENA_Project (European Nucleotide Archive accession number)</li> <li>ENA_Sample (European Nucleotide Archive accession number)</li> <li>Sample_ID (Sample name given by ENA accession)</li> <li>Sample_Locale (Sampling location given by either ENA accession or published study)</li> <li>Sample_Country (Sample country of origin given by either ENA accession or published study)</li> <li>Sample_Continent (Sample continent of origin given by either ENA accession or published study)</li> <li>Reported_Sibling (Drones sampled from the same colony are labelled with the same ENA_Run accession for one of their siblings)</li> <li>Reported_Type (Reported type or subspecies based on details from ENA or published study)</li> <li>Reported_Lineage (Reported lineage based on details from ENA or published study)</li> <li>Notes (Miscellaneous details from published study that may be of interest)</li> </ul> <p> </p>
Elk hair trace minerals and treponeme-associated hoof disease surveillance metadata in the US Pacific Northwest
<p>This is the publicly accessible dataset reporting concentrations of thirteen analyzed minerals from hair using inductively coupled plasma mass spectrometry and relevant metadata from treponeme-associated hoof disease in Pacific Northwest elk. The data presented here were analyzed for the manuscript entitled "Associations between hair trace mineral concentrations and the occurrence of treponeme-associated hoof disease in elk (<em>Cervus canadensis</em>)."</p> <p>Please note that reported mineral concentrations are in their adjusted values and raw forms represented by the column name having ".Raw", (e.g., "Selenium" versus "Selenium.Raw"). Elk ecotype is represented by a four letter abbreviation for either Roosevelt (ROOS) or Rocky Mountain (ROMO). Unknown values for some variables (e.g., age class, county) are denoted with a "U."</p>
Dataset: Green Mark certified buildings metadata from Singapore
<p>In many countries, there are local efforts and incentives to further increase the adoption of greener buildings. This highlights the critical need for buildings to be green and tools to facilitate their transformation for new and existing building owners. Thus, this work curates a dataset regarding the actions and features implemented by building stakeholders to attain the Green Mark (GM) certification which promotes the adoption of numerous green building technologies. Public data from 3,583 entries over 17 years was extracted and pre-processed. Green features of each certified entry were identified and labeled accordingly using a list of keywords created from the GM certification assessment criteria. We present an overview of the dataset and key insights into the building retrofitting landscape. The dataset is available at <a href="https://github.com/buds-lab/data-driven-greenmark">https://github.com/buds-lab/data-driven-greenmark</a> as well as the feature descriptions, raw data processing and raw files. </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.