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.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “perimeter”
Gaviota Fire Perimeter (Santa Barbara County, CA), June 9, 2004 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gaviota Fire burned from 2004-06-05 to 2004-06-12, 15 miles west of Santa Barbara, Santa Barbara County. Approximately 7440 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2004-06-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Tea Fire Perimeter (Santa Barbara County, CA), November 15, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Tea Fire burned from 2008-11-13 to 2008-11-17, Montecito, Cold Springs Creek and Hot Springs Road, Santa Barbara County. Approximately 1940 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-11-15, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Jesusita Fire Perimeter (Santa Barbara County, CA), May 10, 2009 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Jesusita Fire burned from 2008-05-05 to 2008-05-18, Northwest of Mission Canyon and Santa Barbara City, Santa Barbara County. Approximately 8733 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-05-10, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Length, width, perimeter, and sediment thickness of ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on the length, width, perimeter, and sediment thickness measurements of each ice-rafted sediment deposit. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
Ukraine Fire Perimeters 2022
<p>The data set contains the fire perimeters for Ukraine for 2022 sourced from remote sensing data. The locations and dates of burning were detected using MODIS/VIIRS products on thermal anomalies. This information was used to select pre- and post-fire Copernicus Sentinel 2 L2A imagery. Satellite images were screened from clouds, cloud shadows and combined into median composite mosaics. The image mosaics were created using all available imagery within a 14-day time window before (pre-fire mosaic) and after (post-fire mosaic) each fire. Fire perimeters were visually delineated by comparing post and pre-fire image mosaics using a SWIR2–NIR–Red band combination. The spatial accuracy of fire perimeters corresponds to Sentinel 2 data at 20-m spatial resolution.</p> <p>Delineated fire perimeters were intersected with the Copernicus Dynamic Land Cover map at 100 m resolution (v.3.0.1) to extract burned areas of five land cover classes according to the following reclassification scheme of the original pixel values.</p> <table> <tbody> <tr> <td> <p><strong>Land cover class</strong></p> </td> <td> <p><strong>Original pixel values</strong></p> </td> </tr> <tr> <td> <p>Coniferous forest (LC_1)</p> </td> <td> <p>111</p> </td> </tr> <tr> <td> <p>Broadleaved forest (LC_2)</p> </td> <td> <p>112,113,114,115,116</p> </td> </tr> <tr> <td> <p>Other natural landscape (LC_3)</p> </td> <td> <p>121,124,125,126, 20,30,60,90</p> </td> </tr> <tr> <td> <p>Agricultural land (LC_4)</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>Settlement (LC_5)</p> </td> <td> <p>50</p> </td> </tr> </tbody> </table> <p><strong>MetaData</strong></p> <table> <tbody> <tr> <td> <p>Field name</p> </td> <td>Description</td> </tr> <tr> <td>id</td> <td>Unique identifier of each fire perimeter</td> </tr> <tr> <td>longitude</td> <td>Longitude referring to centroids of fire perimeters</td> </tr> <tr> <td>latitude</td> <td>Latitude referring to centroids of fire perimeters</td> </tr> <tr> <td>date</td> <td>Date of burning in the format DD.MM.YYYY</td> </tr> <tr> <td>year</td> <td>Year</td> </tr> <tr> <td>month</td> <td>Month</td> </tr> <tr> <td>day</td> <td>Day of the month</td> </tr> <tr> <td>week</td> <td>Week number in the year</td> </tr> <tr> <td>reg</td> <td>The capital city name of the Ukrainian regions (oblast) associated with each fire</td> </tr> <tr> <td>occupied</td> <td>A binary indicator variable referring to the locations of each fire within the Russian-occupied territory (1) or within the territory controlled by the Government of Ukraine (0) for a given date</td> </tr> <tr> <td>buff_30km</td> <td>A binary indicator variable referring to the location of each fire within a 30-km buffer zone on both sides of the front line. In contrast to a daily progression front line used to detect "occupied" territory, the buffer was calculated using the farthest position of a frontline towards territories controlled by the Government of Ukraine</td> </tr> <tr> <td>emerald</td> <td>A binary indicator variable referring to locations of each fire within the Emerald network</td> </tr> </tbody> </table> <p> Burned areas by land cover (LC) type (in hectares) within the fire perimeter according to the Copernicus Dynamic Land Cover map at 100 m resolution (v.3.0.1)</p> <table> <tbody> <tr> <td>LC_0</td> <td>N/A</td> </tr> <tr> <td>LC_1</td> <td>Coniferous forest</td> </tr> <tr> <td>LC_2</td> <td>Broadleaved forest </td> </tr> <tr> <td>LC_3</td> <td>Other natural landscape</td> </tr> <tr> <td>LC_4</td> <td>Agricultural land </td> </tr> <tr> <td>LC_5</td> <td>Settlement</td> </tr> </tbody> </table>
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Data and codes for "Perimeter modes of nanomechanical resonators exhibit quality factors exceeding 10^9 at room temperature"
<p>Data and codes for "Perimeter modes of nanomechanical resonators exhibit quality factors exceeding 10<sup>9</sup> at room temperature"</p>
Northwestern Europe Wildfire Perimeter Polygons [2012-2022]
<p>This dataset contains KML files for wildfires detected from VIIRS across northwestern Europe from 2012 to 2022. Each file contains the fire progression at each satellite overpass. These files were used to detect the rate of spread for each wildfire event to characterize the fire behavior in temperate northwest Europe, a region with limited fire history. </p>
SmartSystem Virtual Reality Headset Perimeter Versus Humphrey HFA-III Perimeter
ClinicalTrials.gov study NCT05674890. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Moss dominated biological soil crust reduced runoff by increasing wetted perimeter of cross-section
Open the record for dataset details and reuse information.
A controlled vocabulary defining the semantic perimeter of Sustainable Development Goals
<p>A set of controlled terms that define the scope and breadth of <a href="https://sustainabledevelopment.un.org/">Sustainable Development Goals (SDGs) as defined by the United Nations</a>. These terms may be used to tag and index textual records in accordance with SDGs.</p> <p>The vocabulary is constructed by means of the following steps:</p> <ol> <li>An initial set of terms per SDG target is built by extracting key terms from the UN official list of Goals, Targets and Indicators</li> <li>The list is manually enriched by performing a review of the literature produced around SDGs and by compiling lists of pertinent words per Target mentioned by the reviewed documents</li> <li>A reference textual corpus is downloaded by searching for the initial set terms defined at step 1. and 2. The corpus is used to train a Word2Vec word embedding model (a machine learning model based on neural networks).</li> <li>The terms’ list is then enriched by means of automatic methods, which are run in parallel: <ul> <li>The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.</li> <li>Further terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.</li> <li>An automated algorithm is used to retrieve, from the APIs of WikiPedia a series of terms that have some categorical relationships (i.e. those that are indexed as “a broader concept of”, or “equivalent to” in DBpedia) with the initial set of words.</li> </ul> </li> <li>The final list produced by steps 1-4 s finally manually revised</li> </ol>
Modeling wetland functions: Is space-to-time substitution of perimeter-area relation appropriate?
<p>Wetlands' morphometric or shape properties such as their area and perimeter impact a multitude of ecosystem functions and services. However, current models used to quantify these functions often only use area as an independent variable, as the static area and perimeter of different wetlands have been found to be closely related. The study uses monthly satellite-based inundation maps to assess the temporal covariation of geographically isolated wetlands' perimeter and surface area. The results show that using static representations of wetlands to evaluate temporal dynamic perimeter-area relations can introduce significant discrepancies and that these discrepancies can be reduced if evaluations using static data are performed separately for each wetlandscape. The study concludes that current models that use implicit area-perimeter relations based on static wetland representations should be used with caution and that incorporating perimeter-area relations from temporally dynamic data can improve wetland function estimates.</p>
Winged Perimeter Versus Traditional Plastic Biliary Stent for Malignant Bile Duct Obstruction
ClinicalTrials.gov study NCT01514214. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparison of Visual Fields: Humphrey Field Analyzer (HFA) SITA Strategy, SCOPE (GATE Strategy) and Humphrey MATRIX Perimeter
ClinicalTrials.gov study NCT01068938. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PHP Home Preferential Hyperacuity Perimeter for the Detection of Choroidal Neovascularization (CNV)
ClinicalTrials.gov study NCT00359008. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Modeling wetland functions: Is space-to-time substitution of perimeter-area relation appropriate?
Open the record for dataset details and reuse information.
Taxus genotype data for "Trunk perimeter correlates with genetic bottleneck intensity and the level of genetic diversity in populations of Taxus baccata L."
<p>The data set contains microsatellite genotypes (18 loci) of Taxus baccata trees, together with the information about sampling sites.</p>
FIGURE 7. A. Tritubular duct cuticular perimeter thin, B in Rhizoecus colombiensis Ramos & Caballero, a new species of hypogeal mealybug (Hemiptera: Coccomorpha: Rhizoecidae) and a key to the species of Rhizoecus from Colombia
FIGURE 7. A. Tritubular duct cuticular perimeter thin, B. Tubules of tritubular duct thick.
Comparison of Visual Field Assessments Between Three Perimeters
ClinicalTrials.gov study NCT06539715. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Perampanel on Neurophysiology Test Perimeters
ClinicalTrials.gov study NCT03653741. IPD Sharing: NO. Countries: 1. Publications: 0.
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.