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PIE LTER nutrient samples collected by Sigma Autosampler between 2001 and 2017 in three headwater sites of contrasting land use, and at the Parker and Ipswich River Dams as they enter into the Plum Island Sound estuary, Massachusetts.
Total organic nitrogen, total organic phosphorus, and nitrate concentrations collected frequently by Sigma autosampler (or volunteers in winter) from 5 sites. Sites include three headwater sites of contrasting land use (CC= Forest, SB = suburban, CS = wetland) and at the mouth of the Ipswich and Parker Rivers where they flow into the estuary.
Mummichog (Fundulus heteroclitus) counts and gut content analysis from lift trap transect collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The lift traps were used to capture mummichogs accessing the high marsh platform. Mummichogs were collected to study the effect of marsh-edge geomorphology on mummichog distribution and foraging. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Mummichog (Fundulus heteroclitus) gut content analysis from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here we use gut content analysis assess the diet of mummichog on the high marsh platform during a flooding spring-cycle tide in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data allow us to quantify the amount of terrestrial invertebrate prey mummichog consume on a single flooding tide and determine the impact altered low marsh geomorphology has on the trophic relationships in PIE food webs. These mummichog were captured in Breder traps; information about the consumer communities captured in these traps was recorded separately (LTE-TIDE-BrederTrap-Demographics). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
Demographics of high marsh consumers from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here, we capture animals using Breder traps to quantify the communities accessing the high marsh at night during one of these high tides in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data can be used for the assessment of the impact of low marsh geomorphology on consumer communities in PIE marshes. Mummichog captured in these Breder traps were further analyzed for gut content (LTE-TIDE-BrederTrap-GutContents). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
Mummichog (Fundulus heteroclitus) caloric value analysis from various collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here, we use bomb calorimetry to assess individual mummichog caloric content per gram of biomass at 4 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data can be used for the assessment of the impact of low marsh geomorphology on mummichog caloric content and energy production in PIE food webs. These data were included as part of two studies “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and “Cross-habitat access modifies the ‘trophic relay’ in New England saltmarsh ecosystems” (Lesser et al. 2021).
Stable isotope values for organisms collected in the Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
To assess differences in trophic relationships and enerygy transfer related to long term fertilization of tidal creeks (TIDE Project: https://thetideproject.org/), organisms including primary producers, invertebrates, and fish were sampled and analyzed for stable isotopes of nitrogen (δ14N) , carbon (δ13C), and sulfur (δ34S). Collections were made during summers from 2000-2018 from reference and nutrient-enriched branches of 4 tidal creeks.
SBC LTER: Santa Cruz Island: Abundance and Biomass of Benthic Organisms (food resource collection)
These data describe the abundance of benthic organisms as determined by random quadrat scrapings. These data represent the availability of food resources for fish at various depths, and are part of a long term investigation of temporal patterns in reef community composition. The sampling locations in this dataset include three sites along the north shore of Santa Cruz Island. Data collection began in 1982 and this dataset is updated annually.
Field-Collected Spectral Reflectance of Dominant Vegetation at the Sevilleta National Wildlife Refuge
This dataset includes field-collected spectral reflectance of dominant vegetation species in grassland and shrubland at the Sevilleta National Wildlife Refuge collected monthly May – September 2019. A spectroradiometer was used to collect the percent spectral reflectance of electromagnetic radiation (range 400-2500nm) of a sample of dominant vegetation species ("spectra"), yielding a spectral curve for each species. At least ten individuals per species were sampled. These data form a spectral library which was used to calibrate a multiple-endmember spectral mixture analysis (MESMA) of satellite imagery of the Sevilleta NWR, as part of an ongoing collaboration between the LTER and the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Ultimately, we aim to produce fractional images of green vegetation, non-photosynthetic vegetation, bare soil, and shade to form a synoptic thirty-year record of vegetation dynamics at the Refuge. The spectral library can be referenced by future researchers using remote sensing methods to examine vegetation dynamics at the Sevilleta NWR.
Cambridge butterfly collection - Loreto, Peru 2018
<p>Cambridge Butterfly Collection. Loreto, Peru Part 1</p> <p>EN: This upload contains photographs taken by Eva van der Heijden at the Butterfly Genetics Group at the University of Cambridge, from a butterfly wing collection from Loreto, Peru, in collaboration with Green Gold Forestry. Individual sample names can be found in the information sheet. Further Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (<a href="https://heliconius.ecdb.io/">click here for the database</a>, and <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here for FAQ</a>). Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) or Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) for further information.</p> <p> </p> <p>ES: Este repositorio contiene fotografías tomadas por Eva van der Heijden en el Butterfly Genetics Group de la Universidad de Cambridge, de mariposas de Loreto (Peru), en colaboración con la compañía Green Gold Forestry. Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (<a href="https://heliconius.ecdb.io/">haga clic aquí para la base de datos</a>, y <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí para preguntas frecuentes</a>) Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) o Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) con sus preguntas o peticiones.</p>
PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)
<p>As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.</p>
MiRoR5 - P2- Overcoming Barriers to Mobilizing Collective Intelligence in Research: Qualitative Study of Researchers With Experience of Collective Intelligence.
<p>Anonymised data of respondents to an open-ended online survey on their experience with collective intelligence</p>
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
Temperature and ADCP data collected on Lake Geneva between 2015 and 2017
<p>Data collected between 2015 and 2017 on Lake Geneva by Acoustic Doppler Current Profiler (ADCP) and CTDs. One file includes all the temperature profiles, the two others are the ADCP data (up- and down-looking) at the SHL2 station (centre of the main basin). Coordinates of the SHL2 station are 534700 and 144950 in the Swiss CH1903 coordinate system. The file with the CTD data contains the coordinates of the sample location (lat, lon), times (in MATLAB time), depths (in meters) and temperatures (in °C).</p> <p>All files are in MATLAB .mat format.</p>
Raw meteorological dataset from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw text files of meteorology data collected in the Southern Ocean and Atlantic Ocean as part of the Antarctic Circumnavigation Expedition (ACE). Data coverage is from 17th November 2016 until 11th April 2016.</p> <p>Data files have undergone no processing or quality-checking and are as-recorded, directly from the instrumentation.</p> <p>Wind speed and direction parameters were recorded with a resolution of three seconds. Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds.</p> <p>Time of the measurement should be used with the TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p><strong>Dataset contents</strong></p> <ul> <li>MAWS__SMSAWS__YYYYMMDD.txt, data file, ASCII tab-separated</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_raw_meteorology_data_change_log.txt, metadata, text format</li> </ul> <p>Data files contain data for one day and are named by that date.</p> <p>Null values are recorded as ///, // or /</p> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data files with coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added this change_log file.</p> <p><strong>v1.0</strong> - Initial release of raw meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological dataset is made available under the 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>
Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"
<p>This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897</p> <p>Contents: </p> <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> <p>See the readme and summary files contained within the archives for further details.</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2018: Globe
<p>Consolidated epoch 2018 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include </p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2017: Globe
<p>Consolidated epoch 2017 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe
<p>Near real time epoch 2019 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
The Dublin Language Garden Perceptual Dialectology of Irish English Collection
<blockquote> <p><strong>Recommended citation for this dataset:</strong><br> Garnett, Vicky, & Lucek, Stephen. (2020). The Dublin Language Garden Perceptual Dialectology of Irish English Collection (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4247829</p> </blockquote> <p> </p> <p><strong>About this Dataset</strong><br> The field of Perceptual Dialectology is an area of sociolinguistic study that investigates how non-linguists view different varieties of language. It often includes hand-drawn map exercises in which participants indicate where they believe various varieties are spoken, and their attitudes towards them. </p> <p>In 2015, as part of a public linguistics outreach event (the Dublin Language Garden) held at Trinity College Dublin, the authors created an activity for members of the public and collected hand-drawn maps from them that gave responses to the following tasks:</p> <p>a. Indicate where you come from on the map (using a red dot sticker)<br> b. Draw where you think the Dublin dialect occurs<br> c. Draw the boundaries of any other dialects you believe occur in Ireland<br> d. Tell us what you think are the features of those dialects<br> e. Tell us what you think are the characteristics of the people who speak those dialects.</p> <p>Participants of all ages were encouraged to take part, but only data from those over 18 were retained after the event and used in this data collection. Participants were all given information on how the data was to be anonymised, processed and published on a clearly displayed poster to read before they were given a map to complete the 5 tasks (listed above). No additional information about the participants, aside from that acquired through Task a, was collected.</p> <p> </p> <p><strong>File List:</strong></p> <ul> <li>_READ_ME - Dublin Language Garden Perceptual Dialectology of Irish English data.txt<br> Contains a detailed description of this dataset.<br> </li> <li>DLG_PDIE_KML_data_by_location.zip<br> This zipped folder contains the .kml data of multiple hand-drawn maps organised into folders by their location<br> </li> <li>DLG_PDIE_KML_data_by_part.zip<br> This zipped folder contains the .kml data of multiple hand-drawn maps organised into folders according to the participants.</li> </ul> <p>These folders have been organised in this way in order to make discoverability easier between the data. Users may wish to analyse the data only by the locations of the varieties identified by the participants. Other users may only be interested in the data given by specific participants, and therefore the folder that organises the data in this way may be of better use to them. Both folders, however, contain the same data, it is simply how they are organised.</p> <ul> <li>Garnett and Lucek DLG_PD_IE Qualitative Data (Nov 2020).xlsx<br> Spreadsheet featuring tabulated qualitative data taken from all maps<br> </li> <li>Sample Hand-drawn Maps.zip<br> Folder containing 2 sample hand-drawn maps from the participants to help contextualise the data presented here.</li> </ul> <p> </p> <p><strong>Any questions?</strong><br> Any enquiries regarding this dataset should be directed to either Vicky Garnett (<a href="mailto:garnetv@tcd.ie">garnetv@tcd.ie</a>) or Stephen Lucek (<a href="mailto:stephen.lucek@ucd.ie">stephen.lucek@ucd.ie</a>).</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.