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.
40,091
datasets available to search
ShareScore release 0.7.1
Dataset results
40,091 results for “records”
Precipitation measurements from historic and current standard, storage and recording rain gauges at the Andrews Experimental Forest, 1951 to present
Andrews Forest precipitation has been measured continuously using various rain gage types since 1951. Most of these rain gages are standard (non-recording) gages with 7.5 or 8 inch orifices or large capacity storage gages intended for sites with limited access collected irregularly over longer intervals. Recording rain gages have also been established to collect higher temporal resolutions (e.g., 5 minute or 15 minute) and also used as a means of parsing (“prorating”) these periodic interval measurements from these standard and storage gages into daily totals. This data set includes an inventory of all rain gages that have operated within the Andrews as well as one site in the nearby Wildcat RNA and one in the town of Blue River. The inventory includes information regarding the date range of operation, gage location, type of gage, the rain network within which it was established, general availability of data and descriptive notes. A second table includes all of the raw measurement data for these non-recording gages over every interval where data were taken, and additionally includes the corresponding recording gage and its measurement total used to prorate data into a daily record. A third table includes the prorated daily data for all of these standard and storage gages as well as the true daily totals for two recording rain gages. A fourth table includes high temporal resolution for one early recording gage at Forks and the Mack Creek recording gage. Note that while precipitation data associated with the 6 benchmark stations are included in this rain gage inventory (Entity 1), the daily and high temporal resolution data for these sites were available through a separate meteorological data set, database code MS001, until 2025. In 2025, the benchmark station data was migrated here and will be combined with the Forks and Mack Creek data.
Dendrochronological Record in Hemlock Removal Experiment at Harvard Forest 2004-2006
As part of the long-term goal of reconstructing the stand and land-use history of the Simes Tract at Harvard Forest, trees were cored from the 8 hemlock and hardwood plots in the Hemlock Removal Experiment. Cores were sanded and growth was measured. The data were used for the 2006 senior thesis (Division III paper) of Peter Bettman-Kerson at Hampshire College ("Dendrochronological reconstruction of historical disturbances in a hemlock forest at the Harvard Forest, Petersham, MA"). This dataset is the raw growth data for 230 trees sampled across the 8 plots.
Air temperature data for C1 chart recorder, 1952 - ongoing.
Temperature data were collected on a daily time-scale from the C1 climate station (3018 m) since 1952. Over time various circumstances have led to days with missing values. Some missing values were estimated from redundant sensors and nearby climate stations using various methods. Greenland 1987 was a basis for the methodology. However when it was not possible to use this methodology, new methods were developed.
Air temperature data for D1 chart recorder, 1952 - ongoing.
Temperature data were collected on a daily time-scale from the D1 climate station (3743 m) since 1952. Over time, various circumstances have led to days with missing values. Some missing values were estimated from redundant sensors and nearby climate stations using various methods. Greenland 1987 was a basis for the methodology. However when it was not possible to use this methodology, new methods were developed.
Precipitation data for D1 chart recorder, 1964 - ongoing.
Precipitation data were collected on a daily timescale from the D1 climate station (3743 m) since 1964. Over time, various circumstances have led to days with missing values. Some of these values were estimated from nearby climate stations.
Precipitation data for Saddle chart recorder, 1981 - ongoing.
Precipitation data were collected on a daily time-scale from the Saddle climate station (3525 m) since 1981. Over time, various circumstances have led to days with missing values. Some of these values were estimated from nearby climate stations for years up to 2008 using the methods described in METHODS.
Human MEG recordings during sequential conflict task
Open the record for dataset details and reuse information.
Long-term record of lake and stream biogeochemistry from the Loch Vale Watershed, Rocky Mountain National Park, Colorado, USA: 1981-2024
The Loch Vale Watershed (LVWS) Project is a long-term research and monitoring program that addresses watershed-scale ecosystem processes, particularly as they respond to atmospheric deposition and climate variability. The LVWS is a 7-km2 high-altitude basin located within Rocky Mountain National Park in the Colorado Front Range (Colorado, United States of America). This dataset includes year-round measurements of physical water parameters, nutrients, major ions, trace metals, silica, and chlorophyll collected from lakes and streams within the LVWS basin. Related data entities: Scanned field notebooks from the Loch Vale Watershed Project from 1981-2023 are available via this published data release: https://www.sciencebase.gov/catalog/item/6723cba2d34e4f57573e8e45. Quality assurance reports from the Loch Vale Watershed Project are available for specific time periods and can be found at the following locations: 1983-1987: included in this data release under "Other Entities", file name LWVS_QAreport_1983to1987_Denning 1988: included in this data release under "Other Entities", file name LWVS_QAreport_1988_Denning 1989-1990: included in this data release under "Other Entities", file name LWVS_QAreport_1989to1990_Edwards 1995-1998: https://doi.org/10.3133/ofr99111 1999-2002: https://doi.org/10.3133/ofr20041306 2003-2009: https://doi.org/10.3133/ofr20111137 2010-2019: https://doi.org/10.3133/tm1D9 The most recent methods manual is included in full in this data release under "Other Entities", file name "LVWS Methods Manual". Please refer to this manual for the detailed methods.
DBS Phantom Recordings
Open the record for dataset details and reuse information.
Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system
Open the record for dataset details and reuse information.
Sky irradiance over photosynthetically active radiation wavelengths (400-700 nm) recorded shipboard during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains high resolution records of sky irradiance over photosynthetically active radiation wavelengths (PAR; 400-700 nm) recorded shipboard during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. A hemispherical PAR sensor was fixed to the bow of the RV Akademik Tryoshnikov ship at approximately 2 metres height above the main deck and continuously recorded the irradiance over PAR wavelengths (400-700 nm) at 1 minute intervals from 21st December 2016 to the 16th March 2017. This data provides high resolution information on the diel cycle in sky irradiance and absolute sky irradiance over PAR wavelengths (400-700 nm) along the ship track of the ACE expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_par_20200526CURRSGCMR.csv, data file, comma-separated values</li> </ul>
Ethnic and Migrant Minorities (EMM) Survey Registry: All metadata records
<p>The <a href="https://ethmigsurveydatahub.eu/emmregistry/">Ethnic and Migrant Minorities (EMM) Survey Registry</a> is a free online tool that allows users to search for and learn about existing quantitative surveys undertaken with EMM (sub)populations conducted in 34 European countries, from 2000 onwards, through compiled survey-level metadata.</p> <p>The first version was produced by a team led by CEE (Sciences Po, CNRS) and jointly funded through the COST Action 16111 – ETHMIGSURVEYDATA (a network of more than 200 European researchers active in the ethnic and migration studies field), the Horizon 2020 infrastructure project SSHOC (within Task 9.2 on Ethnic and Migration Studies, within Work Package 9 on Data Communities) and the project FAIRETHMIGQUANT (an Open Science project funded by the French Agence Nationale de la Recherche, ANR).</p> <p>This specific record includes the metadata for 2,120 survey records as .dta, .sav and .csv files published on the Registry, as of 31.07.2025.</p>
OHHR – The Oldenburg Hearing Health Record [Dataset]
<p><strong>Description of the dataset</strong></p> <p>The Oldenburg Hearing Health Record (OHHR) provides a publicly accessible dataset that can be used to advance hearing health research. It includes a constellation of data collected from 581 participants (aged 18–86 years<em>; </em>255 female; <em>Mean age = 67.31 years; SD = 11.93</em>) between 2013 and 2015 at the Hörzentrum Oldenburg in collaboration with the Cluster of Excellence "Hearing4all". The data was anonymized in accordance with the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679). Each participant was assigned a unique identifier to maintain anonymity while enabling multivariate individualized analyses. </p> <p>All the different data types are listed below:<br><br><strong>Subjective Measures</strong></p> <ul> <li>Home Questionnaire</li> <li>SF-12 Health Survey</li> <li>Technology Readiness Questionnaire</li> <li>Anamnesis</li> </ul> <p><strong>Audiological Tests</strong></p> <ul> <li>Pure Tone Audiometry</li> <li>Adaptive Categorical Loudness Scaling</li> <li>Digit Triplet Test (Speech Reception Threshold in Noise: Screening)</li> <li>Göttingen Sentence Test (Speech Reception Threshold in Noise)</li> </ul> <p><strong>Cognitive Measures</strong></p> <ul> <li>DemTect</li> <li>WortSchatz</li> </ul> <p><strong>Demographic Information</strong></p> <ul> <li>Socio-economic data</li> <li>Scheuch-Winkler Index (calculated)</li> </ul> <p><strong>Supporting Documentation</strong></p> <p><strong>MethodsDescription.rtf/.pdf:</strong> Provides detailed explanations of data type and collection procedures.<br><strong>data.zip/metadata</strong><strong>: </strong>Includes schema and description files for all data tables.</p> <p>A supporting paper was published on Scientific Data:</p> <div> <div> <p>Jafri, S., Berg, D., Buhl, M. <em>et al.</em> The Oldenburg Hearing Health Record (OHHR). <em>Sci Data</em> <strong>12</strong>, 1546 (2025). https://doi.org/10.1038/s41597-025-05884-y</p> </div> </div>
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
Occurrence Record Dataset from "Annotated checklist of the bees of Bonaire, with a focus on host plants"
<p>This is the occurrence dataset created for the publication "Annotated checklist of the bees of Bonaire, with a focus on host plants" (<a href="https://natuurtijdschriften.nl/pub/1026875" target="_blank" rel="noopener">https://natuurtijdschriften.nl/pub/1026875</a>).</p> <p>Observation and specimen data were assembled for this dataset, with the majority of records obtained during the Bonaire Estafette Expeditie (BEE). All citizen science records from Observation.org and iNaturalist.org up to December 2023 have been critically reviewed.<br>A project was created (<a href="https://www.inaturalist.org/projects/flower-visitors-and-pollinators-of-the-caribbean" target="_blank" rel="noopener">Flower visitors and pollinators of the Caribbean</a>) to improve standardized data collecting of plant-pollinator interactions and on <a href="https://observation.org/">observation.org</a> the standardized fields for interactions were used.<br>Records from passive trapping methods are not included. All bees were either observed or collected by hand or insect net. The majority of specimens will be accessible in the collection of Naturalis Biodiversity Center (RMNH), Leiden (the Netherlands). A synoptic collection is retained at the University of Tartu Zoological Collections in Tartu, Estonia (TUZ).</p> <p>The occurrence dataset (Version 1.4 and later) is:</p> <ul> <li>conform Darwin Core (DwC): <a href="https://dwc.tdwg.org/terms/">https://dwc.tdwg.org/terms</a></li> <li>in the data format CSV (tab delimited values) and UTF-8 encoded</li> </ul> <p> </p> <p><strong>DwC terms (Column labels) used in the dataset with their description:</strong></p> <table> <tbody> <tr> <td><strong>Column label</strong></td> <td><strong>Column description</strong></td> </tr> <tr> <td>occurrenceID</td> <td>Unique identifier or URI (GUID) for each record, mainly unique URLs generated by the web-based data holder.</td> </tr> <tr> <td>catalogNumber</td> <td>Unique code derived from URI in occurrenceID. Each specimen bears a label with this identifier and multimedia are tagged with this identifier.</td> </tr> <tr> <td>recordNumber</td> <td>Sample field ID used to manage data of preserved specimen occurrence records.</td> </tr> <tr> <td>otherCatalogNumbers</td> <td>Other unique identifiers used on specimen labels, but not derived from an URI.</td> </tr> <tr> <td>scientificName</td> <td>The scientific name of the lowest taxonomic rank to which the individual(s) was identified.</td> </tr> <tr> <td>scientificNameAuthorship</td> <td>The author name and year of publication in accordance with ICZN rules.</td> </tr> <tr> <td>verbatimIdentification</td> <td>The original identification, including qualifiers if needed.</td> </tr> <tr> <td>individualCount</td> <td>The number of individuals present at the time of the occurrence.</td> </tr> <tr> <td>sex</td> <td>The sex of the individual(s). The values female, male or unknown are used, if a mixed group is observed multiple values are listed.</td> </tr> <tr> <td>lifeStage</td> <td>The life stage of the individual(s).</td> </tr> <tr> <td>basisOfRecord</td> <td>The specific nature of the data record at the time of the identification (e.g. PreservedSpecimen).</td> </tr> <tr> <td>identifiedBy</td> <td>The name of the person who made the identification in the field or based on collected evidence (e.g. specimen or photo).</td> </tr> <tr> <td>identificationQualifier</td> <td>In case the identification could be given only to a species group 'cf.' is recorded.</td> </tr> <tr> <td>dateIdentified</td> <td>The year when the identification was made.</td> </tr> <tr> <td>previousIdentifications</td> <td>The scientific name originally given to the observed or collected individual(s).</td> </tr> <tr> <td>order</td> <td>The name of the order (e.g. Hymenoptera).</td> </tr> <tr> <td>family</td> <td>The name of the family (e.g. Apidae).</td> </tr> <tr> <td>genus</td> <td>The name of the genus (e.g. Apis).</td> </tr> <tr> <td>subgenus</td> <td>The name of the subgenus (e.g. Apis).</td> </tr> <tr> <td>specificEpithet</td> <td>The name of the species, epithet as given in dwc:scientificName.</td> </tr> <tr> <td>taxonRank</td> <td>The taxonomic rank of the most specific name in dwc:scientificName.</td> </tr> <tr> <td>eventDate</td> <td>The date-time when the event was observed and recorded. The event date uses the ISO 8601-1:2019 standard, with the following formatting being used: format YYYY-MM-DD, or YYYY if only the year is known. If time of capture is known, then format is YYYY-MM-DDTHH:MM, with HH:MM the local time.</td> </tr> <tr> <td>year</td> <td>The year in which the event was observed and recorded.</td> </tr> <tr> <td>month</td> <td>The month in which the event was observed and recorded.</td> </tr> <tr> <td>day</td> <td>The day in which the event was observed and recorded.</td> </tr> <tr> <td>eventTime</td> <td>The time or interval during which the event occurred.</td> </tr> <tr> <td>samplingProtocol</td> <td>The name or description of the collecting or recording method used.</td> </tr> <tr> <td>behavior</td> <td>A description of the behavior shown by the individual(s) recorded in this occurrence.</td> </tr> <tr> <td>decimalLatitude</td> <td>The geographic latitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>decimalLongitude</td> <td>The geographic longitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>geodeticDatum</td> <td>The ellipsoid, geodetic datum, or spatial reference system (SRS) upon which the geographic coordinates given in dwc:decimalLatitude and dwc:decimalLongitude is based.</td> </tr> <tr> <td>verbatimLocality</td> <td>The original textual description of the place.</td> </tr> <tr> <td>island</td> <td>The name of the island.</td> </tr> <tr> <td>countryCode</td> <td>The standard ISO 3166-1 alpha-2 country code for the country.</td> </tr> <tr> <td>coordinateUncertaintyInMeters</td> <td> <p>The horizontal distance (in meters) from the given dwc:decimalLatitude and dwc:decimalLongitude describing the smallest circle containing the actual location, usually the EPE (Estimated Position Error) from the GPS device. The EPE is here measured as the horizontal position error in meters.</p> </td> </tr> <tr> <td>recordedBy</td> <td>A person, group, or organization observing and recording the occurrence.</td> </tr> <tr> <td>associatedTaxa</td> <td>The type of association and the scientific name of the host taxon is recorded that is associated/has relationship with the taxon in dwc:scientificName. The association/relationship is recorded using the format as in the following example: "floral host":"Lantana sp."</td> </tr> <tr> <td>occurrenceRemarks</td> <td>Comments or notes about the dwc:Occurrence.</td> </tr> <tr> <td>associatedSequences</td> <td>A list (concatenated and separated) of identifiers (publication, global unique identifier, URI) of genetic sequence information.</td> </tr> <tr> <td>typeStatus</td> <td>A list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</td> </tr> <tr> <td>collectionCode</td> <td>The name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</td> </tr> <tr> <td>identificationRemarks</td> <td>Comments or notes about the identification.</td> </tr> <tr> <td>identificationReferences</td> <td>A reference or list of references (publication, global unique identifier, URI) used for the identification.</td> </tr> <tr> <td>nameAccordingTo</td> <td>A reference to the checklist or publication that was followed to record the name in dwc:scientificName.</td> </tr> <tr> <td>samplingEffort</td> <td>The amount of effort, expressed in minutes or hours, to obtain and record the occurrences.</td> </tr> <tr> <td>occurrenceStatus</td> <td>A statement about the presence or absence of a taxon during the time of an event.</td> </tr> <tr> <td>disposition</td> <td>The current state of a specimen with respect to a collection.</td> </tr> <tr> <td>language</td> <td>The language of the record using ISO 639-1 codes, e.g. en</td> </tr> </tbody> </table>
Antarctic Circumnavigation Expedition event log: recording data and sample collection in the Southern Ocean during the austral summer of 2016/17.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) spent 90 days circumnavigating Antarctica on the R/V Akademik Tryoshnikov during the austral summer of 2016/17. This dataset provides a record of the instrument deployments as well as dataset and sample collection events that took place during the expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_events.csv, data file, comma-separated values</li> <li>sampling_method_descriptions.csv, metadata, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This event log is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p> </p>
Reconstructed high-rate SEIS data recorded during HP3 hammering from the NASA InSight mission to Mars
<p>The NASA InSight lander successfully placed a seismometer on the surface of Mars. Alongside, a hammering device was deployed that penetrated into the ground to attempt the first measurements of the planetary heat flow of Mars. The hammering of the heat probe generated repeated seismic signals that were registered by the seismometer. However, the broad frequency content of the seismic signals generated by the hammering extends beyond the Nyquist frequency governed by the seismometer's sampling rate of 100 samples per second. Here, we provide data that was reconstructed at a higher sampling rate of 2000 samples per second using a dedicated de-aliasing algorithm described in the accompanying article. This archive will be updated regularly with new data acquired on Mars. </p> <p>For a detailed data description and instructions on how to cite this dataset, please refer to the README file. </p>
Khotanese Manuscripts from Chinese Turkestan in the British Library (XML records)
<p>The file contains XML records matching the print edition of Skjaervo's catalogue, in TEI schema P4.</p> <p>The records in this file are a <strong>draft version</strong>. They have not yet been proofed and checked against physical holdings, which will be done with the next version release.</p> <p>The XML records have been produced as part of the work for the project <em>Beyond Boundaries: Religion, Region, Language and the State</em> (An ERC Synergy project from the European Research Council under the EU's 7th Framework Programme (FP7/2007-2013)/ERC grant agreement no.609823)</p>
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Interaction
<h1>The THÖR-MAGNI Dataset Tutorials</h1> <p>THÖR-MAGNI datasets is a novel dataset of accurate human and robot navigation and interaction in diverse indoor contexts, building on the previous <a href="https://ieeexplore.ieee.org/abstract/document/8954833/">THÖR dataset protocol</a>. We provide position and head orientation motion capture data, 3D LiDAR scans and gaze tracking. In total, THÖR-MAGNI captures <strong>3.5 hours of motion of 40 participants on 5 recording days</strong>.</p> <p>This data collection is designed around systematic variation of factors in the environment to allow building cue-conditioned models of human motion and verifying hypotheses on factor impact. To that end, THÖR-MAGNI encompasses 5 scenarios, in which some of them have different conditions (i.e., we vary some factor):</p> <ul> <li>Scenario 1 (plus conditions A and B): <ul> <li> Participants move in groups and individually;</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles and lane marking on the floor for <strong>condition B</strong>;</li> </ul> </li> </ul> <ul> <li> Scenario 2: <ul> <li> Participants move in groups, individually and transport objects with variable difficulty (i.e. bucket, boxes and a poster stand);</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 3 (plus conditions A and B): <ul> <li> Participants move in groups, individually and transporting objects with variable difficulty (i.e. bucket, boxes and a poster stand). We denote each role as: <em>Visitors-Alone, Visitors-Group 2, Visitors-Group 3, Carrier-Bucket, Carrier-Box, Carrier-Large Object;</em></li> <li> Teleoperated robot as moving agent: in <strong>condition A</strong>, the robot moves with differential drive; in <strong>condition </strong>B, the robot moves with omni-directional drive;</li> <li> Environment with 2 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 4 (plus conditions A and B): <ul> <li> All participants, denoted as <em>Visitors-Alone HRI</em> interacted with the teleoperated mobile robot;</li> <li> Robot interacted in two ways: in <strong>condition A</strong> (Verbal-Only), the Anthropomorphic Robot Mock Driver (ARMoD), a small humanoid NAO robot on top of the mobile platform, only used speech to communicate the next goal point to the participant; in <strong>condition B</strong> the ARMoD used speech, gestures and robotic gaze to convey the same message;</li> <li> Free space environment</li> </ul> </li> </ul> <ul> <li>Scenario 5: <ul> <li> Participants move alone (<em>Visitors-Alone</em>) and one of the participants, denoted as <em>Visitors-Alone HRI</em>, transport objects and interact with the robot;</li> <li> The ARMoD is remotely controlled by an experimenter and proactively offers help;</li> <li> Free space environment;</li> </ul> </li> </ul> <h2>Preliminary steps</h2> <p>Before proceeding, make sure to download the data from ZENODO</p> <h3>1. Directory Structure</h3> <p>├── CLiFF_Maps <- Directory for CLiFF Maps for all files</p> <p> ├── Files <- Directory for the csv files</p> <p> ├── Readme.md</p> <p>├── CSVs_Scenarios <- Directory for aligned data for all scenarios</p> <p> ├── Scenario_1 <- Directory for the csv files for Scenario 1</p> <p> ├── Scenario_2 <- Directory for the csv files for Scenario 2</p> <p> ├── Scenario_3 <- Directory for the csv files for Scenario 3</p> <p> ├── Scenario_4 <- Directory for the csv files for Scenario 4</p> <p> ├── Scenario_5 <- Directory for the csv files for Scenario 5</p> <p>├── docs</p> <p> ├── tutorials.md <- Tutorials document on how to use the data</p> <p>├── Lidar_sample</p> <p> ├── Files <- Directory for sample files</p> <p> ├── 170522_SC3B_1 <- Directory for the pcd files</p> <p> ├── 170522_SC3B_1.csv <- Synchronization file with QTM</p> <p> ├── manual_view_point.json <- json file with manual view point for visualization</p> <p> ├── requirements.txt <- script pip requirements</p> <p> ├── visualize_pcd.py <- script visualize the lidar data</p> <p> ├── Readme.md</p> <p>├── maps <- Directory for maps of the environment (PNG files) and offsets (json file)</p> <p> ├── offsets.json <- Offsets of the map with respect to the global coordinate frame origin</p> <p> ├── {date}_SC{sc_id}_map.png <- Maps for `date` in {1205, 1305, 1705, 1805} and `sc_id` in {1A, 1B, 2, 3}</p> <p> ├── 3009_map.png <- Map for the Scenarios 4A, 4B and 5</p> <p>├── MP4_Videos</p> <p> ├── Files <- Directory for the mp4 files</p> <p> ├── pupil_scene_camera_instrinsics.json <- json file with the intrinsics of pupil camera</p> <p>├── TSVs_RAWET <- Directory for the TSV files for the Raw Eyetracking data for all Scenarios</p> <p> ├── synch_info.csv <- Event markers necessary to align motion capture with eyetracking data</p> <p> ├── Files <- Directory with all the raw eyetracking TSV files</p> <p>├── goals_positions.csv <- File with the goals locations</p> <p> </p> <h3>2. Data Structure and Dataset Files</h3> <p>Withing each Scenario directory, each csv file contains:</p> <p><strong>2.1. Headers</strong></p> <p>The dataset metadata overview contains important information found in the CSV file headers. This reference is designed to help users understand and use the dataset effectively. The headers include details such as FILE_ID, which provides information on the date, scenario, condition, and run associated with each recording. The header of the document includes important quantities such as the number of frames recorded (N_FRAMES_QTM), the count of rigid bodies (N_BODIES), and the total number of markers (N_MARKERS).</p> <p>It also provides information about the order of the contiguous rotation matrix (CONTIGUOUS_ROTATION_MATRIX), modalities measured with units, and specified measurement units. The text presents details on the eyetracking devices used in each recording, including their infrared sensor and scene camera frequencies, as well as an indication of the presence of eyetracking data.</p> <p>The header provides specific information about rigid bodies, including their names (BODY_NAMES), role labels (BODY_ROLES), and the number of markers associated with each rigid body (BODY_NR_MARKERS). Finally, the table lists all marker names used in the file.</p> <p>This metadata provides researchers and practitioners with essential guidance on recording information, data quantities, and specifics about rigid bodies and markers. It is a valuable resource for understanding and effectively using the dataset in the CSV files.</p> <p><strong>2.2. Trajectory Data</strong></p> <p>The remaining portion of the CSV file integrates merged data from the motion capture system and eye tracking devices, organized based on participants' helmet rigid bodies. Columns within the dataset include XYZ coordinates of all markers, spatial centroid coordinates, 6DOF orientation of the object's local coordinate frame, and <em>if available</em> eye tracking data, encompassing 2D/3D gaze coordinates, scene recording frame numbers, eye movement types, and IMU data.</p> <p>Missing data is denoted by "N/A" or an empty cell. Temporal indexing is facilitated by the "Time" or "Frame" column, indicating timestamps or frame numbers. The motion capture system records at 100Hz, Tobii Glasses at 50Hz (Raw); 25 Hz (Camera), and Pupil Glasses at 100Hz (Raw); 30 Hz (Camera). The dataset is structured around motion capture recordings, and for each rigid body, such as "Helmet_1," details per frame include XYZ coordinates of markers, centroid coordinates, and a 9-element rotational matrix describing helmet orientation.</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td>Helmet_1 - 1 X</td> <td>X-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Y</td> <td>Y-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Z</td> <td>Z-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - [...]</td> <td><em>Same for Marker 2 and 3 of Helmet_1</em></td> </tr> <tr> <td>Helmet_1 Centroid_X</td> <td>X-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Y</td> <td>Y-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Z</td> <td>Z-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 R0</td> <td>1st Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td>Helmet_1 R[..]</td> <td>Same for R1- R7</td> </tr> <tr> <td>Helmet_1 R8</td> <td>9th Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> </tbody> </table> <p> </p> <p><strong>2.3. Eyetracking Data</strong></p> <p>The eye tracking data in the dataset includes 16 participants, providing a comprehensive dataset of over 500 minutes of recorded data across the different activities and scenarios with three different eyetracking devices. Devices are denoted with a special "Tracker_ID" in the dataset, i.e.:</p> <table> <tbody> <tr> <td><strong>Tracker ID</strong></td> <td><strong>Eyetracking Device</strong></td> </tr> <tr> <td>TB2</td> <td>Tobii 2 Glasses</td> </tr> <tr> <td>TB3</td> <td>Tobii 3 Glasses</td> </tr> <tr> <td>PPL</td> <td>Pupil Insivisible Glasses</td> </tr> </tbody> </table> <p>Gaze points are classified into fixations and saccades using the Tobii I-VT Attention filter, which is specifically optimized for dynamic scenarios with a velocity threshold of 100°. Eyetracking devices were systematically repeated after each 4-minute recording to account for natural variations in participants' eye shapes and to improve the gaze estimation algorithms. In addition, gaze estimation adjustments for the pupil invisible glasses were made after each 4-minute recording to mitigate potential drifts. It's worth noting that the scene cameras of the eye tracking glasses had different fields of view. The scene camera of the Pupil Invisible Glasses had a 1088x1080 image with both horizontal (HFOV) and vertical (VFOV) opening angles of 80°, while the Tobii Glasses provided a 1920x1080 image with different opening angles for Tobii Glasses 3 (HFOV: 95°, VFOV: 63°) and Tobii Glasses 2 (HFOV: 82°, VFOV: 52°).</p> <p><strong>NOTE AS OF 2024:</strong> <strong>Videos are NOW part</strong> of the dataset</p> <p>For one participant, wearing the Tobii Glasses 3 and Helmet_6, the data would be denoted as:</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td><em>Helmet_6 - [...]</em></td> <td><em>*X,Y,Z Coordinates for 5 markers*</em></td> </tr> <tr> <td><em>Helmet_6 [...]</em></td> <td><em>X,Y,Z Coordinates for 1 Centroid* </em></td> </tr> <tr> <td><em>Helmet_6 R[...]</em></td> <td><em>9 Elements of the CONTIGUOUS_ROTATION_MATRIX</em></td> </tr> <tr> <td> <p>Helmet_6 TB3_Accelerometer_[...]</p> </td> <td>Accelerometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Gyroscope_[...]</td> <td>Gyroscope data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Magnetometer_[...]</td> <td>Magnetometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_G2D_[...]</td> <td>2D Eye tracking data (X,Y)</td> </tr> <tr> <td>Helmet_6 TB3_G3D_[...]</td> <td>3D Cyclopic Eye gaze Vector (X,Y,Z)</td> </tr> <tr> <td>Helmet_6 TB3_Movement</td> <td>Eye movement type (N/A, Fixation or Saccade)</td> </tr> <tr> <td>Helmet_6 TB3_SceneFNr</td> <td>Frame number of the scene camera recording </td> </tr> </tbody> </table> <h2>How to use and tools</h2> <p><a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">magni-dash</a></p> <p><a href="https://magni-dash.streamlit.app">This</a> is a dashboard to quickly visualize our data: trajectories, speeds, eye-tracking data and LiDAR visualization (for Scenario 3). If you cannot use the dashboard from the streamlit cloud service, just run it locally by following the <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">README File</a>.</p> <p><a href="https://github.com/tmralmeida/thor-magni-tools">thor-magni-tools</a></p> <p>To install and use the package, follow the instructions on the <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/README.md">README file</a> . This package comprises:</p> <ul> <li>3D trajectory restoration: agents in the scene wore an helmet. The helmet is equipped with markers, which are tracked by the Mocap system. 3D trajectory restoration stands for <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/thor_magni_tools/preprocessing/cfg.yaml#L3">two different ways</a> of aggregating the trackings of the various markers in each helmet: (1) <em>3D-restoration</em> and (2) <em>3D-best marker</em>. The former applies an average over the locations of all visible markers while the latter uses the marker with highest tracking duration.</li> <li>3D pre-processing of restored trajectories: interpolation, downsampling and smoothing. To run the 3D pre-processing, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#preprocessing">this</a>.</li> <li>trajectory analysis: trajectory-related metrics like tracking duration (in seconds), number of 8s <em>tracklets</em>, motion speed, path efficiency score, and minimal distance between people. To run the trajectory analysis, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#analysis">this</a>.</li> </ul>
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.