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.
657
datasets available to search
ShareScore release 0.9.0
Dataset results
657 results for “July”
Biomass of Spartina alterniflora collected in July 2018 from various marsh edges around Plum Island Sound, MA, PIE LTER.
Biomass of Spartina alterniflora was collected at various locations within the Plum Island Sound estuary. Samples were collected during July 2018. Only Spartina alterniflora was collected. To collect biomass, 25 X 25 cm quadrats were placed over the plants. Aboveground biomass was clipped to soil surface. Biomass was dried in the lab in a drying oven until a constant weight was reached, and then weighed. Biomass values were then convertedto grams of dry weight per square meter (g/m2).
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Twitter historical dataset: March 21, 2006 (first tweet) to July 31, 2009 (3 years, 1.5 billion tweets)
<p><strong>Disclaimer: </strong>This dataset is distributed by Daniel Gayo-Avello, an associate professor at the Department of Computer Science in the University of Oviedo, for the sole purpose of non-commercial research and it just includes tweet ids.</p> <p>The dataset contains tweet IDs for <strong>all the published tweets</strong> (in any language) <strong>bettween March 21, 2006 and July 31, 2009</strong> thus comprising the first whole three years of Twitter from its creation, that is, about <strong>1.5 billion tweets</strong> (see file <em>Twitter-historical-20060321-20090731.zip</em>).</p> <p>It covers several defining issues in Twitter, such as the invention of hashtags, retweets and trending topics, and it includes tweets related to the 2008 US Presidential Elections, the first Obama’s inauguration speech or the 2009 Iran Election protests (one of the so-called Twitter Revolutions).</p> <p>Finally, it does contain tweets in many major languages (mainly English, Portuguese, Japanese, Spanish, German and French) so it should be possible–at least in theory–to analyze international events from different cultural perspectives.</p> <p>The dataset was completed in November 2016 and, therefore, the tweet IDs it contains were publicly available at that moment. This means that there could be tweets public during that period that do not appear in the dataset and also that a substantial part of tweets in the dataset has been deleted (or locked) since 2016.</p> <p>To make easier to understand the decay of tweet IDs in the dataset a number of representative samples (99% confidence level and 0.5 confidence interval) are provided.</p> <p>In general terms, <strong>85.5% ±0.5 of the historical tweets are available as of May 19, 2020</strong> (see file <em>Twitter-historical-20060321-20090731-sample.txt</em>). However, since the amount of tweets vary greatly throughout the period of three years covered in the dataset, additional representative samples are provided for 90-day intervals (see the file <em>90-day-samples.zip</em>).</p> <p>In that regard, the ratio of publicly available tweets (as of May 19, 2020) is as follows:</p> <ul> <li>March 21, 2006 to June 18, 2006: 88.4% ±0.5 (from 5,512 tweets).</li> <li>June 18, 2006 to September 16, 2006: 82.7% ±0.5 (from 14,820 tweets).</li> <li>September 16, 2006 to December 15, 2006: 85.7% ±0.5 (from 107,975 tweets).</li> <li>December 15, 2006 to March 15, 2007: 88.2% ±0.5 (from 852,463 tweets).</li> <li>March 15, 2007 to June 13, 2007: 89.6% ±0.5 (from 6,341,665 tweets).</li> <li>June 13, 2007 to September 11, 2007: 88.6% ±0.5 (from 11,171,090 tweets).</li> <li>September 11, 2007 to December 10, 2007: 87.9% ±0.5 (from 15,545,532 tweets).</li> <li>December 10, 2007 to March 9, 2008: 89.0% ±0.5 (from 23,164,663 tweets).</li> <li>March 9, 2008 to June 7, 2008: 66.5% ±0.5 (from 56,416,772 tweets; see below for more details on this).</li> <li>June 7, 2008 to September 5, 2008: 78.3% ±0.5 (from 62,868,189 tweets; see below for more details on this).</li> <li>September 5, 2008 to December 4, 2008: 87.3% ±0.5 (from 89,947,498 tweets).</li> <li>December 4, 2008 to March 4, 2009: 86.9% ±0.5 (from 169,762,425 tweets).</li> <li>March 4, 2009 to June 2, 2009: 86.4% ±0.5 (from 474,581,170 tweets).</li> <li>June 2, 2009 to July 31, 2009: 85.7% ±0.5 (from 589,116,341 tweets).</li> </ul> <p>The apparent drop in available tweets from March 9, 2008 to September 5, 2008 has an easy, although embarrassing, explanation.</p> <p>At the moment of cleaning all the data to publish this dataset there seemed to be a gap between April 1, 2008 to July 7, 2008 (actually, the data was not missing but in a different backup). Since tweet IDs are easy to regenerate for that Twitter era (source code is provided in <em>generate-ids.m</em>) I simply produced all those that were created between those two dates. All those tweets actually existed but a number of them were obviously private and not crawlable. For those regenerated IDs the actual ratio of public tweets (as of May 19, 2020) is 62.3% ±0.5.</p> <p>In other words, what you see in that period (April to July, 2008) is not actually a huge number of tweets having been deleted but the combination of deleted *and* non-public tweets (whose IDs should not be in the dataset for performance purposes when rehydrating the dataset).</p> <p>Additionally, given that not everybody will need the whole period of time the earliest tweet ID for each date is provided in the file <em>date-tweet-id.tsv</em>.</p> <p>For additional details regarding this dataset please see: <strong>Gayo-Avello, Daniel. "How I Stopped Worrying about the Twitter Archive at the Library of Congress and Learned to Build a Little One for Myself." <em>arXiv preprint arXiv:1611.08144</em> (2016)</strong>.</p> <p><strong>If you use this dataset in any way please cite that preprint </strong>(in addition to the dataset itself).</p> <p>If you need to contact me you can find me as <a href="https://twitter.com/pfcdgayo">@PFCdgayo</a> in Twitter.</p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Metabolomics.Info People Portal Data as of 8 July 2020
<p>Metabolomics.Info People Portal Data as of 8 July 2020 in <a href="https://www.w3.org/TR/n-triples/">N-Triples</a> format.</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>
TESS-JULY-2020
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). July 2020</p>
Covid19 case data July 2020
<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 July 2020.</p>
Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000
<p>This includes data from the EPREM+CORHEL simulation run presented in "Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000" (Astrophysical Journal). The eight files ending in '.nc' contain the EPREM stream-observer data used to create Figures 5 & 7. The data was saved in the self-describing <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF4</a> format. The HTML files contain the following interactive figures, which you can open in your internet browser:</p> <ul> <li><strong>cos_theta-e10.0-t44.html</strong> cosine of the flow angle (Figure 7)</li> <li><strong>divV-e10.0-t44.html</strong> velocity divergence (Figure 7)</li> <li><strong>flux-e10.0-t44-log.html</strong> differential flux of 10-MeV protons (Figure 7)</li> <li><strong>peak_flux-e10.0-t44.html</strong> relative peak flux of 10-MeV protons (Figure 5)</li> <li><strong>peak_flux-e100.0-t44.html</strong> relative peak flux of 100-MeV protons (not shown in paper)</li> <li><strong>tau_p-e10.0-t44-log.html</strong> theoretical acceleration rate (Figure 7)</li> </ul>
Data from: "Discovery and characterization of 80 SNPs and 1,624 SSRs in the transcriptome of Atlantic mackerel (Scomber scombrus, L)" in Genomic Resources Notes Accepted 1 June 2015 to 31 July 2015
This paper reports on SNP discovery in the Atlantic mackerel transcriptome, using next generation sequencing technologies and applying developed methodology already proven successful for the European anchovy. A total of 9,966 high quality transcriptome contigs were assembled, from which 951 putative SNPs were discovered. In all, 479 putative SNPs and 1,624 simple sequence repeats (SSRs) suitable for genotyping were identified. A subset of 96 was selected for genotyping; from these, 80 SNPs were considered polymorphic and reliably scored after genotyping of 105 individuals from three locations in the Eastern Atlantic Ocean. These markers will be valuable for future studies on population genetic structure assessment and for product tracing.
Data from: Jorge M G, Brennand T A. Measuring (subglacial) bedform orientation, one-dimensional size, and directional shape - method accuracy. Submitted to PLOS ONE, July 2015
<p>Georeferenced (EPSG 32610) vector file (shapefile format) containing the longitudinal subglacial bedform footprint dataset used in the study.<br /> </p>
- Nomenclature Section of the XVII International Botanical Congress, Vienna, Austria, July 2005. – Photograph by Rudolf Hromniak. Previously published including a key to the persons depicted in Taxon 59(4) 2010 1306–1307.
- Nomenclature Section of the XVII International Botanical Congress, Vienna, Austria, July 2005. – Photograph by Rudolf Hromniak. Previously published including a key to the persons depicted in Taxon 59(4) 2010 1306–1307.
Figure 2. - Pupae of Phengarisalcon in a Myrmicascabrinodis nest. Locality Placy near Příbram in Central Bohemia, 1 July 2015. Photo: Ondřej Sedláček.
Figure 2. - Pupae of Phengarisalcon in a Myrmicascabrinodis nest. Locality Placy near Příbram in Central Bohemia, 1 July 2015. Photo: Ondřej Sedláček.
Figure 2. - Pupae of Phengarisalcon in a Myrmicascabrinodis nest. Locality Placy near Příbram in Central Bohemia, 1 July 2015. Photo: Ondřej Sedláček.
Figure 2. - Pupae of Phengarisalcon in a Myrmicascabrinodis nest. Locality Placy near Příbram in Central Bohemia, 1 July 2015. Photo: Ondřej Sedláček.
Supplementary material 1: Number of Pteridaceae species by gender in Togo from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Number of Pteridaceae species by gender in Togo
Figure 10. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 10. - Ceratopteristhalictroides: fertile frond.
Figure 14b. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 14b. - PterisatrovirensFigure 14a.PinnaeFigure 14b.Pinnules <br> Pinnules
Figure 14a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 14a. - PterisatrovirensFigure 14a.PinnaeFigure 14b.Pinnules <br> Pinnae
Figure 2. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 2. - Species diversity by gender within Pteridaceae of Togo (Suppl. material 1).
Figure 9a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 9a. - AdiantumvogeliiFigure 9a.Entire frondFigure 9b.Details of pinnae and sori <br> Entire frond
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.