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13,021 results for “locality”
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Great Britain's hourly natural gas demand at a local level (distribution level) from 2017-01 to 2018-03
<p>An hourly local natural gas demand dataset created for the UK Energy Research Centre's Phase 3 FlexiNET project.</p> <p>A briefing note using part of the data can be found - http://www.ukerc.ac.uk/publications/local-gas-demand-vs-electricity-supply.html</p> <p>The dataset has been aggregated from the hourly operational demand data from Great Britain's four Gas Distribution Network companies (GDNs) and provides an empirical record rather than modelled data.</p> <p>Columns ['utc_index', 'utc_aware', 'utcdiff', 'localtime_aware', 'localtime_naive_text', 'localtimediff', 'gb_demand_kWh_cleaned']</p> <p>Column descriptions:</p> <p>utc_index: datetime in utc</p> <p>utc_aware: datetime in utc timezone aware</p> <p>utcdiff: difference between subsequent values for utc_aware column (as check step value - should only be 0 days 01:00:00.000000000)</p> <p>localtime_aware: London local time</p> <p>localtime_naive_text: London local time as text</p> <p>localtimediff: difference between subsequent values for localtime_aware column (as check step value - should have one value per year of 02:00 hours for clock forward in March to British Summer Time, 00:00 for clock backward in October, and 01:00 for all other values)</p> <p>gb_demand_kWh_cleaned: the aggregate demand for natural gas through the local gas networks in kWh over the hour</p>
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Local plot information observed on LandKlif plots during vegetation survey 2019
<p><span>LandKlif local plot information observed on site during vegetation survey 2019, including vegetation height, slope, aspect, proximity to hedge / forest edge / water, intensity of use (only for meadows), and further information on plot habitat.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Zebrafish Pathway Metabolite MetFrag Local CSV
<p>This is a local CSV file of Zebrafish metabolites for MetFrag (https://msbi.ipb-halle.de/MetFrag/) extracted from PubChem, based partially on previous data extracted from Wikipathways, KEGG and literature (DOI: <a href="https://doi.org/10.1371/journal.pone.0213661">10.1371/journal.pone.0213661</a>), combined in previous versions of this record (DOI: <a href="https://doi.org/10.5281/zenodo.3541624">10.5281/zenodo.3541624</a>).</p> <p>This file was created as documented on the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/tree/main/taxonomy/Danio_rerio">ECI GitLab</a>. </p> <p>This file is designed for identification using MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p> </p>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Local Ecological Knowledge and folk medicine in historical Esthonia, Livonia, Courland and Galicia, 1805-1905
<p>Background: Historical ethnobotanical data can provide valuable information about past human-nature relationships as well as serve as a basis for diachronic analysis. This thesis aims to document medicinal plant uses in the 19th century mentioned in German-language sources in the historical regions of Esthonia, Livonia, Courland and Galicia to analyse the gathered data in regard to plant families and medicinal use categories and finally to qualitatively compare the results with various studies from the study area and surrounding regions with recently acquired data as well as historical data.</p> <p>Methods: Data was mainly obtained by systematic manual search in various relevant historical German-language works focused on the medicinal use of plants. Data about plant and non-plant constituents, their usage, the mode of administration, used plant parts and their German and local names was extracted and collected into a database in the form of Use Reports.</p>
MetFrag Local CSV: CompTox (7 March 2019 release) MetaData File
<p>These are the CSV files that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, updated versions of these files are integrated in the web interface.</p> <p>This upload includes the SelectMetaData version of the CompTox MetFrag file from the 7 March 2019 release (DOI:<a href="https://doi.org/10.23645/epacomptox.7525199.v2">10.23645/epacomptox.7525199.v2</a>).</p>
Seasonal hindcast of temperature and precipitation at a local scale by using TeWA approach
<p><strong>Methodology</strong></p> <p>Data set of simulated time-series of temperature and precipitation for the 1982-2020 period. Our statistical seasonal prediction model have two main components: a) the ocean-atmosphere coupling represented by correlations between surface variables with delayed teleconnections and b) the self-predictability of the residual anomalies by trends or cycles (quasi-oscillations).</p> <p>The approach has three stages approach with two main predictor components, as mentioned above. The first two stages consist of separate predictions, one per each component, and the third stage is a combination of both predictions (Fig. 2): Teleconnection-based approach (Redolat et al. 2019, 2020) and a self-predictability by using Wavelet-ARIMA models (Conejo et al. 2005; Joo and Kim 2015). Therefore, the total method is a Teleconnection+Wavelet+ ARIMA (TeWA) approach.</p> <p><strong>References</strong></p> <p>Conejo, A.J., M.A. Plazas, R. Espinola, A.B. Molina, 2005: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst., 20, 1035-1042, https://doi.org/10.1109/TPWRS.2005.846054.</p> <p>Joo, T., S. Kim, 2015: Time series forecasting based on wavelet filtering. Expert Syst. Appl. 42, 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026</p> <p>Redolat, D., R. Monjo, C. Paradinas, J. Pórtoles, E. Gaitán, C. Prado-López, and J. Ribalaygua, 2020: Local decadal prediction according to statistical/dynamical approaches. Int. J. Climatol., 40: 5671–5687. https://doi.org/10.1002/joc.6543.</p> <p>Redolat, D.; R. Monjo, J.A. Lopez-Bustins, and J. Martin-Vide, 2019: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theor. Appl. Climatol., 135: 1059–1077. https://doi.org/10.1007/s00704-018-2424-6.</p>
Local explanation SHAP approach applied to MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions
<p>The repository contains materials for analysing the results of the Local explanation named SHAP-CTREE (Redelmeier et al., 2020) approach applied to the MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions from Goelzer et al. (2020).</p> <p>The available files are:<br> - run_SupplMat.R: the main R script to perform the diagnostics and the different analyses (levels 1 - 3)<br> - utilsPLOT.R: functions for plotting<br> - Diagnostics.zip: the zip file with the png figures, named 'GrIS_CaseXXX_yYYY.png', that depict the diagnostic for case XXX for prediction time YYY<br> - SupplementaryMaterials.zip<br> - RData files for each prediction time YYY "Shapley_yYYY" with:<br> S: matrix N=55 cases x d+1: SHAP values for the d inputs (+ average sea level value at time YYY)<br> YHAT: ML-based predictions of the sea level for the 55 cases<br> YTRUE: true values for the 55 cases<br> mae: mean absolute error<br> - RData file containing the design of experiments "DOE_GrIS_MIROC5-RCP85.RData"<br> doe: matrix with values of the d=9 inputs</p> <p>These constitute the supplementary materials of Rohmer et al. (2022, The Cryosphere). All technical details are provided in this reference.</p>
Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones
<p>Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones (in J. Acoust. Soc. Am., 2024)</p>
SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization
<h1><strong>Abstract</strong></h1> <p>This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. <br><br>SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms.<br>To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available <a href="https://github.com/remaro-network/SubPipe-dataset">online.</a></p> <p>On Zenodo we provide <em>three</em> versions for SubPipe. One is the full version (<strong>SubPipe.zip</strong>, ~80GB unzipped) and two subsamples: <strong>SubPipeMini.zip</strong>, ~12GB unzipped and <strong>SubPipeMini2.zip</strong>, ~16GB unzipped. Both subsamples are only parts of the entire dataset (SubPipe.zip). SubPipeMini is a subset, containing semantic segmentation data, and it has interesting camera data of the underwater pipeline. On the other hand, SubPipeMini2 is mainly focused on underwater side-scan sonar images of the seabed including ground truth object detection bounding boxes of the pipeline.</p> <p><strong>For (re-)using/publishing SubPipe, please include the following copyright text:</strong></p> <p><em><strong>SubPipe</strong> is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p><em>More information about OceanScan-MST can be found at </em><a href="https://www.oceanscan-mst.com/"><em>this link</em></a><em>.</em></p> <h1><strong>Cam0 — GoPro Hero 10</strong></h1> <h4>Camera parameters:</h4> <ul> <li>Resolution: 1520×2704</li> <li>fx = 1612.36</li> <li>fy = 1622.56</li> <li>cx = 1365.43</li> <li>cy = 741.27</li> <li>k1,k2, p1, p2 = [−0.247, 0.0869, −0.006, 0.001]</li> </ul> <h1><strong>Side-scan Sonars</strong></h1> <p>Each sonar image was created after 20 “ping” (after every 20 new lines) which corresponds to approx. ~1 image / second.</p> <p>Regarding the object detection annotations, we provide both COCO and YOLO formats for each annotation. A single COCO annotation file is provided per each chunk and per each frequency (low frequency vs. high frequency), whereas the YOLO annotations are provided for each SSS image file.</p> <p>Metadata about the side-scan sonar images contained in this dataset:</p> <table> <tbody> <tr> <td><strong>Images for object detection</strong></td> <td> </td> </tr> <tr> <td># Low Frequency (LF):</td> <td> 5000</td> </tr> <tr> <td>LF image size:</td> <td>2500 × 500</td> </tr> <tr> <td># High Frequency (HF):</td> <td> 5030</td> </tr> <tr> <td>HF Image size</td> <td>5000 × 500</td> </tr> <tr> <td><strong>Total number of images:</strong></td> <td>10030</td> </tr> <tr> <td><strong>Annotations</strong><strong><br></strong></td> <td> </td> </tr> <tr> <td># Low Frequency:</td> <td> 3163</td> </tr> <tr> <td># High Frequency:</td> <td> 3172</td> </tr> <tr> <td><strong>Total number of annotations:</strong></td> <td><strong> </strong>6335</td> </tr> </tbody> </table>
Natural Products Atlas (NPAtlas) MetFrag Local CSV
<p>This is a local CSV file of the Natural Products Atlas (NPAtlas, <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was extracted to CSV from the TSV download from the NPAtlas <a href="https://www.npatlas.org/download">website</a>, with column headers for compulsory fields adjusted to fit the MetFrag format. Several entries with charged formulas (one +3, 7 +2, 125 +, 6 negative) had the charges removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation). Several overflowing lines were removed (due to new metadata) and NPA023832 was removed as "Ho" is not recognised by MetFrag. </p> <p>This file is for users wanting to integrate the latest NPAtlas into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY - details at <a href="https://www.npatlas.org/">https://www.npatlas.org/</a></p>
MetFrag Local CSV: CompTox (7 March 2019 release) Smoking MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p><a>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</a></p> <p>The Smoking MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>- PubMedNeuro: the <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">LITMINEDNEURO</a> list with total PubMed reference counts in the column</p> <p>- <a href="https://comptox.epa.gov/dashboard/chemical_lists/CIGARETTES">CIGARETTES</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/INDOORCT16">INDOORCT16</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SRM2585DUST">SRM2585DUST</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SLTCHEMDB">SLTCHEMDB</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/THSMOKE">THSMOKE</a> as suspect lists.</p> <p>First release (July 2019, not archived) contained a smaller subset of the SRM2585DUST list.</p>
WormJam Metabolites Local CSV for MetFrag
<p>This is a local CSV file of WormJam (https://www.tandfonline.com/doi/full/10.1080/21624054.2017.1373939) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>The text file provided by Michael (also part of this dataset) was modified into CSV by adding identifiers and adjusting headers for MetFrag import. </p> <p>This CSV file is for users wanting to integrate WormJam into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Update 10 Sept 2019: curated truncated InChIKey, InChI entries, added missing SMILES, added DTXSIDs by InChIKey match.</p>
YMDB2.0 MetFrag Local CSV
<p>This is a local CSV file of YMDB 2.0 (http://www.ymdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>Data was extracted to CSV from the SDF, with column headers for compulsory fields adjusted to fit the MetFrag format. One entry with no SMILES was filled in using the InChI in OpenBabel; entries with no monoisotopic mass or formula were filled in using functions in RChemMass (https://github.com/schymane/RChemMass/), finally one generic formula (row 746) was replaced with the formula from the InChI. </p> <p>This file is for users wanting to integrate the latest YMDB into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</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.