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517 results for “October”
Biweekly soil-moisture in the 18 CARBONO Project plots, La Selva Biological Station, March 1998-October 2018
Open the record for dataset details and reuse information.
Biweekly fine litterfall in the 18 CARBONO Project plots, La Selva Biological Station, October 1997-October 2018
Open the record for dataset details and reuse information.
Soil fungal community and activity under dominant plant species at the Sevilleta National Wildlife Refuge, New Mexico: October 2013
We measured fungal composition and activity in the soil under dominant plant species across a grassland to shrubland transition to determine if shrubs cultivate soil microbial communities as they encroach grasslands. The project was conducted in the northern Chihuahuan desert at the Sevilleta National Wildlife Refuge (site of the Sevilleta LTER) in New Mexico on October 20, 2013. Soils were collected under one shrub (Larrea tridentata) and three grass (Bouteloua eriopoda, Bouteloua gracilis, Pleuraphis jamesii) species in grassland, shrubland, and the ecotone region between grasslands and shrublands. Soil fungal communities were evaluated using DNA amplicon sequencing and fungal activity was quantified using several extracellular enzyme activity assays. Soil texture and soil chemistry were also measured.
Precipitation disdrometer at C1 from October 2013 to May 2016
An OTT Parsivel Disdrometer is used to generate detailed precipitation information on the fall speeds and sizes of hydrometeors at the Niwot Ridge C1 site. This disdrometer data aids in the quantification of rain and snowfall microstructure allowing users of the data to investigate the influence that hydrometeor characteristics have on precipitation accumulation rates, streamflow/runoff, precipitation gauge errors, and radar reflectivity-rain/snow rate relationships (these are current uses of the data but there are many more possibilities). Parameters, such as intensity of precipitation, radar reflectivity, and visibility, are derived from the particle size and speed quantities and are complimentary to the other precipitation instrumentation at C1. The type of precipitation is derived from empirical relationships of fall speeds and volume-equivalent diameters and is useful for determining transitions between rain, graupel, snow, etc. The disdrometer at C1 fits into the larger goal of improving our understanding of orographic precipitation and the hydrometeorological dynamics at play on the eastern slopes of the Colorado Front Range.
AIOSAT datasets for Gent's integration phase in October 2019
<p>Datasets from the two days of integration and testing of the second version of the AIOSAT hardware in Gent, around the parking lot defined as the test scenario in Belgium, hosted by Hulpverleningszone Centrum.</p> <p>This dataset presents the data from 3 firefighter nodes with information about GNSS; UWB interdistance, IMU, Lora and Bluetooth Long Range communication systems</p>
WRF v3.9 evaluation statistics, April and October 2015 Ireland
<p>Dataset related to Chapter 4 of PhD thesis "Modelling of accidental radioactive releases for Ireland" By Cillian Joy.<br> This folder contains a file for each statistic used. Time series data is aggregated for each month of the WRF v3.9 evaluation, April and October 2015.<br> Data is for each station (28), meteorology variable (wind speed, wind direction, and precipitation) for each statistic (7 for wind speed and precipitation, and 3 for wind direction.<br> For each location, the value provided is for the month. For example, Fractional Bias for one location for the month is an average of all time steps (1464).</p> <p>The seven statistic are:</p> <ol> <li>Factor of data within a factor (FAC 1.5)</li> <li>Factor of data within a factor (FAC 2.0)</li> <li>Fractional Bias (FB)</li> <li>Normalised Mean Square Error (NMSE)</li> <li>Correlation (R)</li> <li>Geometric Mean Bias (GMB)</li> <li>Geometric Mean Variance (GMV)</li> </ol> <p>21 May 2020, Cillian Joy, cillian.joy@nuigalway.ie</p>
Flick SMART multi-catch rodent station and bait station data sets: Council of the city of Sydney, October 2019 to July 2020
<p>Shortly after the enactment of preventative measures aimed at limiting the spread of COVID-19, local governments and public health authorities around the world reported an increased sighting of rats. We combined multi-catch rodent station data, rodent bait stations data, and rodent-related residents' complaints data to explore the effects that social distancing and lockdown measures might have had on the rodent population within the City of Sydney, Australia. We found that rodent captures, activity, and rodent related residents' complaints increased during the COVID-19 related lockdown period, followed by a steep decline post-lockdown. We found no changes in the geographical distribution of any of our indices of rodent abundance. We hypothesize that lockdown measures resulted in an increase in rodent activity driven by a reduction in human-derived food resources. This might have increased the mortality rate triggering a population crash. There is a high chance that the surviving individuals might be rodenticide resistant. It is possible that the onset of COVID-19 might have disrupted commensal rodent populations, with profound implications for the future management of these species. Here we make available multi-catch rodent station data and rodent bait stations data. We do not include rodent-related residents' complaints data due to potential identifier data that could be seen as a breach of private information sharing.</p>
Covid19 case data October 2020 - The Netherlands
<p>This data is the daily case data on covid-19 cases in the Netherlands, published by the RIVM (national public health agency). The data covers all case data of October 2020.</p>
Gladiator Lamp - Leipzig, Germany - October 2016
Another thing from my apartment in Leipzig. This replica oil lamp is from the living room. Source: Objaverse 1.0 / Sketchfab
452,000,000 public Git commits on GitHub (October 2016)
<p>What's inside</p> <p>part-000xx.lzo - LZO archives with the data (refer to "Format").</p> <p>part-000xx.lzo.index - LZO index files so that the archives are splittable in Hadoop.</p> <p>stats.csv.gz - GZIP-ed CSV file with some repository statistics related to the commits.</p> <p>Format</p> <p>part-000xx - text, one line per repository, every line is JSON with the following scheme:</p> <p>{ "r": "repository name", "c": [{ "h": "git hash", "a": "author's email hash", "t": "date and time commit was created", "m": "commit message" }, ...] }</p> <p>Date and time format is <em>mostly</em> Go language's time.Time.String(), I recommend to use dateutil.parse() to parse it with Python.</p> <p>Commit message contains explicit \r and \n symbols in order to be a single line.</p> <p>stats.csv has 4 columns: repository name, number of commits, number of contributors, average length of the commit messages.</p>
TESS October
<p>Dataset with the measures taken by the STARS4ALL photometers network</p>
Figure 21. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 21. - Eupolybothrus cavernicolus Komerički & Stoev sp. n., paratype, 3D model, volume rendering, created with CTVox, virtual rotation and dissection. Movie available at: YouTube.
Figure 22. from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
Figure 22. - Movie of Eupolybothrus cavernicolus Komerički & Stoev sp. n., holotype, filmed ex-situ in a plastic container. Movie available at: YouTube.
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v3.0.2 - October 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v3.0.2)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>October 18, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/October-18%2C-2021">here</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.