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26 results for “Built-up”
Map of built-up expansion ("nedbygging") over Norway 2017-2022 version 2
<p>Version 2 of the dataset https://zenodo.org/records/10566644</p> <p>Changes from first version include:</p> <ul> <li>added crowdsourced verification labels to the dataset gathered from the interactive app (link below) explained here: https://www.nina.no/Om-NINA/Aktuelt/Nyheter/article/kartlegg-nedbygging-av-natur-selv</li> <li>added the year of change crowdsourced labels</li> <li>added the type of built-up expansion as labelled by the NRK team</li> </ul> <p>Data can be viewed interactively here: <a href="https://nina.earthengine.app/view/nedbygging">https://nina.earthengine.app/view/nedbygging</a></p> <h4>(see Norwegian description below)</h4> <h2> 1. Dataset Information</h2> <p> - Title: Map of built-up expansion over Norway 2017-2022</p> <p> - Author(s): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Contact Information: zander.venter@nina.no</p> <p> - Date of Data Generation: 06.01.2024</p> <p> - Version: 1</p> <p> - Description: This is the dataset used in the <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem-ar-1.16573560">NRK article</a> published on 06.01.2024. The data contains polygons outlining potential “nedbygging” (hereafter translated to “built-up expansion” in English) events between 2017 and 2022 over Norway. The built-up expansion polygons were identified using a combination of Sentinel-2 satellite imagery, a fully convolutional neural network (a type of AI model) from Google called <a href="https://dynamicworld.app/">Dynamic World</a> and NINA’s time series analysis thereof. The method to create the map will be published by NINA at a later date. The original map was created by NINA, but NRK performed some post-processing which included joining some polygons which were part of the same built-up expansion event (e.g. a long road). It is important to note that the map is a result of AI and has errors in it. Therefore, users are encouraged to read the sections on data quality and usage information below. Users can refer to <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a> for details on the scientific best practice which the NRK journalists followed to ensure that their reported area estimates in the article were not biased. In summary, the map is wrong 18% of the time. Users should expect to find that on average 1 in 5 square meter is incorrectly identified as built-up expansion. There are also many instances of built-up expansion which will be missed in the map such as forestry road development, building of small cabins etc. </p> <h2> 2. File Details</h2> <p> - Format: Shapefile (.shp, .shx, .dbf, .prj)</p> <p> - Size: 13.27 MB</p> <h2> 3. Geospatial Information</h2> <p> - Coordinate System: EPSG:32632, UTM zone 32N</p> <p> - Spatial Resolution: 10m</p> <p> - Geographical Coverage: Norway mainland (excludes Svalbard)</p> <p> - Temporal Coverage: 2017 to 2022</p> <h2> 4. Data Content</h2> <p> - Attributes Included:</p> <p> - *id*: unique identity number for each polygon</p> <p> - *undersøkt*: whether the polygon has been investigated manually using visual interpretation of orthophotos. “ja” = “yes” and “nei” = “no”</p> <p> - *<span><span>undersøkt_source*: whether the data was collected by the NRK team or the crowdsourcing effort</span></span></p> <p><span><span> - *</span></span><span><span>kategori_1*: the type of built-up expansion labelled by the NRK team - see Google Translate for translations</span></span></p> <p><span><span> - *year*: the year in which the built-up expansion occurred as defined by the crowdsourcing volunteers</span></span></p> <p> - *ai_feil*: whether the AI model method correctly (“riktig”) or incorrectly (“feil”) identified natural habitat conversion to built-up surface. Values where *undersøkt* == “nei” are labelled as “ikke_verifisert” </p> <h2> 5. Data Quality</h2> <p> - Accuracy: As described above, the false positive rate of the map was 18% based on 500 locations used for map validation and accuracy assessment. We did not quantify a false negative rate and balanced accuracy estimates because this would have required a denser sample for manual verification. Therefore, it is likely that there are many instances of built-up expansion that our map does not capture. After the formal accuracy assessment using the 500 stratified random points, NRK verified additional polygons (total of 3875) in the dataset during their investigative journalism workflow. Although these were not collected in a systematic manner, then can still be useful for some downstream tasks such as exploring what causes the AI model to misidentify built-up expansion.</p> <p> - Validation Methods: A design-based approach was used to quantify map accuracy and estimate uncertainty around the resulting area estimate reported in the NRK article. The details of this method are reported in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. This approach quantifies the error in the AI-derived map, and corrects for this using a stratified area estimator. Therefore, the total built-up expansion of 208 km<2> reported in the NRK article has been bias-corrected. We also quantified 95% confidence intervals around this are estimate of 9.8 km<2>. It is important to note that the validation approach was conducted on individual Sentinel-2 pixels of 10x10m and not at the polygon level. Therefore, we did not quantify the error in the precision of the polygon shape in terms of capturing the full extent of a given built-up expansion event.</p> <h2> 6. Usage Information</h2> <p> - Use Limitations: Considering the map error described above, users should proceed with caution when analysing the map to derive area statistics or overlays with other maps. As described in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, simply adding the areas of the polygons (or “pixel counting” with maps formatted as images) without accounting for the error in the map will lead to incorrect area statistics. We recommend that users validate the map for their municipality or study area before proceeding with analysis. It is likely that the margin of error is highly variable between municipalities. For example, although we have not quantified it, we noticed many AI mistakes in mountainous regions due to snow and ice interference and therefore high-altitude municipalities might have more errors than low-altitude ones.</p> <p> </p> <h1> Norwegian description: </h1> <h2> 1. Datasettinformasjon</h2> <p> - Tittel: Kart over nedbygging over Norge 2017-2022</p> <p> - Forfatter(e): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Kontaktinformasjon: zander.venter@nina.no</p> <p> - Dato for datagenerering: 06.01.2024</p> <p> - Versjon: 1</p> <p> - Beskrivelse: Dette er datasettet som brukes i <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem%20-ar-1.16573560">NRK-artikkelen</a> publisert 06.01.2024. Dataene inneholder polygoner som skisserer potensiell nedbygging mellom 2017 og 2022 over Norge. Nedbyggingsområdene ble identifisert ved hjelp av en kombinasjon av Sentinel-2 satellittbilder, et fullstendig konvolusjonelt nevralt nettverk (en type KI-modell) fra Google kalt <a href="https://dynamicworld.app/">Dynamic World</a> og NINAs tidsserie-analyse av dette. Metoden for å lage kartet vil bli publisert av NINA på et senere tidspunkt. Det originale kartet ble laget av NINA, men NRK utførte en del etterbehandling som inkluderte sammenføyning av noen polygoner som var en del av den samme oppbygde utvidelseshendelsen (f.eks. en lang vei). Det er viktig å merke seg at kartet er produsert ved hjelp av kunstig intelligens og inneholder feil. Derfor oppfordres brukere til å lese avsnittene om datakvalitet og bruksinformasjon nedenfor. Brukere kan referere til <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024) </a>for detaljer om den vitenskapelige beste praksisen som NRK-journalistene fulgte for å sikre at deres rapporterte arealstatistikk i artikkelen er korrekt. Oppsummert er 18 % av arealet i kartet feil. Brukere bør forvente å finne at i gjennomsnitt 1 av 5 kvadratmeter er feilaktig identifisert som nedbygging. Det er også mange tilfeller av nedbygging som som ikke vil vises i kartet, som skogsveiutbygging, bygging av småhytter mm.</p> <h2> 2. Fildetaljer</h2> <p> - Format: Shapefil (.shp, .shx, .dbf, .prj)</p> <p> - Størrelse: 13,27 MB</p> <h2> 3. Geospatial informasjon</h2> <p> - Koordinatsystem: EPSG:32632, UTM-sone 32N</p> <p> - Rolig oppløsning: 10m</p> <p> - Geografisk dekning: Norges fastland (ekskluderer Svalbard)</p> <p> - Tidlig dekning: 2017 til 2022</p> <h2> 4. Datainnhold</h2> <p> - Attributter inkludert:</p> <p> - *id*: unikt identitetsnummer for hver polygon</p> <p> - *undersøkt*: om polygonet er undersøkt manuelt ved bruk av visuell tolkning av ortofoto.</p> <p> - *undersøkt_source*: om dataene er samlet inn av NRK-teamet eller crowdsourcing-innsatsen</p> <p> - *kategori_1*: typen nedbygging merket av NRK-teamet </p> <p> - *year*: året hvor nedbygging skjedde som definert av crowdsourcing</p> <p> - *ai_feil*: om AI-modellmetoden var “riktig” eller “feil”. Verdier der *undersøkt* == «nei» er merket som «ikke_verifisert»</p> <h2> 5. Datakvalitet</h2> <p> - Nøyaktighet: Som beskrevet ovenfor var andelen falske positive punkter i kartet 18 % basert på 500 steder (prøveflater) brukt for kartvalidering og nøyaktighetsvurdering. Vi kvantifiserte ikke andelen falske negative punkter og balanserte nøyaktighetsestimater, fordi dette ville ha krevd en tettere stikkprøvedensitet for manuell verifisering. Derfor er det sannsynlig at det er mange tilfeller av nedbygging som kartet vårt ikke fanger opp. Etter den formelle nøyaktighetsvurderingen ved bruk av 500 stratifiserte tilfeldige prøveflater, verifiserte NRK ytterligere polygoner (totalt 3875) i datasettet i løpet av deres journalistiske undersøkelser. Selv om disse ikke ble samlet inn på en systematisk måte, kan de fortsatt være nyttige for noen oppfølgingsanalyser som å utforske hva som får AI-modellen til å feilidentifisere nedbygging.</p> <p> - Valideringsmetoder: En designbasert tilnærming («design-based area estimation» på engelsk) ble brukt for å kvantifisere kartnøyaktighet og estimere usikkerhet rundt det resulterende arealestimatet rapportert i NRK-artikkelen. Detaljene ved denne metoden er forklart i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. Denne tilnærmingen kvantifiserer feilen i det KI-avledede kartet, og korrigerer for dette ved å bruke en stratifisert arealestimator. Derfor er den totale bebygde utvidelsen på 208 km<2> som er rapportert i NRK-artikkelen, skjevhetskorrigert. Vi kvantifiserte også 95 % konfidensintervaller rundt dette er estimat på 9,8 km<2>. Det er viktig å merke seg at valideringstilnærmingen ble utført på individuelle Sentinel-2 piksler på 10x10m og ikke på polygonnivå. Derfor kvantifiserte vi ikke feilen i presisjonen til polygonformen når det gjelder å fange opp hele omfanget av en gitt oppbygget ekspansjonshendelse.</p> <h2> 6. Bruksinformasjon</h2> <p> - Bruksbegrensninger: Med tanke på kartfeilen beskrevet ovenfor, bør brukere fortsette med forsiktighet når de analyserer kartet for å beregne områdestatistikk eller overlegg med andre kart. Som beskrevet i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, ved å legge til områdene til polygonene (eller «pikseltelling» med kart formatert som bilder) uten å ta hensyn for feilen i kartet vil føre til feil arealstatistikk. Vi anbefaler at brukere validerer kartet for sin kommune eller studieområde før de går videre med analyse. Det er sannsynlig at feilmarginen varierer mye mellom kommuner. For eksempel, selv om vi ikke har kvantifisert det, la vi merke til mange AI-feil i fjellområder på grunn av snø- og is, og derfor kan kommune med mye fjell ha flere feil enn de langs kysten (for eksempel).</p>
Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap
<p>This dataset contains input, intermediary, and output files for the following paper:</p> <p>Yann Forget, Catherine Linard and Marius Gilbert. "<em>Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap</em>", 2018.</p> <p>The dataset is composed of three archives:</p> <ul> <li><code>input.zip</code> : contains raw input data required to run the study ;</li> <li><code>intermediary.zip</code> : contains processed data required for the analysis ;</li> <li><code>output.zip</code> : contains the output tables and images of the study.</li> </ul> <p>Alternatively, output images of the study can be previewed <a href="http://maupp.ulb.ac.be/page/forget2018/">here</a> in interactive maps.</p> <p>The source code used to produce the outputs is availabe <a href="https://zenodo.org/record/1292005">here</a>.</p>
AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites
<p>AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites</p> <p>The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)]. </p> <p>Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year. </p> <p>All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes. </p> <p>Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad). </p> <p>More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated: </p> <p>Vegetation Indices </p> <p>(Atmospherically Resistant Vegetation Index, Kaufman 1972) </p> <p>(Enhanced Vegetation Index, Huete 2002): </p> <p>(Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994) </p> <p>(Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012) </p> <p>(Normalised Difference Moisture Index, Gao 1996) </p> <p>(Normalized Difference Vegetation Index, Rouse 1973) </p> <p>(Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996) </p> <p>(Simple Ratio Vegetation Index Jordan, C.F.1 969) </p> <p>(Soil Adjusted Vegetation Index, Huete 1988) </p> <p>(Visible Atmospherically Resistant Index, Gittleson 2002) </p> <p> </p> <p>Built-up Indexes </p> <p>(Band Ration For Built-up Area, Waqar 2012) </p> <p>(Built-up Area Extraction Index, Bouzekri 2015) </p> <p>(Built-up Index, He Et Al. 2010) </p> <p>(Index-based Built-up Index, Xu 2008) </p> <p>(New Built-up Index, Jieli Et Al. 2010) </p> <p>(Normalized Difference Built-up Index, Zha 2003) </p> <p>(Normalized Built-up Area Index, Waqar 2012) </p> <p>(Urban Index, Kawamura 1996) </p> <p> </p> <p>Water Indices </p> <p>(Modified Normalized Difference Water Index, Xu 1996) </p> <p>(Normalized Difference Water Index, Mcfeeters 1996) </p> <p>(Water Index, Fisher 2016) </p> <p> </p> <p>Other Indices </p> <p>(Bare Soil Index, Rikimaru Et Al. 2002) </p> <p>(Bare Soil Index, Wanhui 2004) </p> <p>(Burn Area Index, Martin 1998) </p> <p>(Clay Minerals Ratio, Drury 1987) </p> <p>(Ferrous Minerals Ratio, Segal 1982) </p> <p>(Iron Oxide Ratio, Segal 1982) </p> <p>(Normalized Burn Ratio, Lopez Garcia 1991) </p> <p>(Normalised Difference Snow Index, Hall 1995). </p>
Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa
<p>The dataset contains input and output files for the following paper:</p> <p>Yann Forget, Michal Shimoni, Marius Gilbert and Catherine Linard. <em>"Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa"</em>. In Press. 2018.</p> <p>The source code used to produce the output files is available on <a href="https://github.com/yannforget/landsat-sentinel-fusion">Github.</a></p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
<p>The changes of urban built-up areas can reflect the process of urbanization, and it can reflect the population, economy, and cultural development of the city. Therefore, accurate and timely extraction of urban built-up areas plays an important role in the dynamic management of the city. In the existing research, single-source remote sensing data is used to extract urban built-up areas, and there is a problem that the spectrum of urban areas and non-urban areas is easily confused. Multi-source remote sensing data, including luojia-1 remote sensing data, Landsat 8 OLI remote sensing data, etc., can make up for the spectrum confusing issues.</p> <p>We fuse the time series information of night light remote sensing data, neighborhood information and point of interest (POI) data in spatial dimension, and propose a built-up area extraction method that integrates night light time and space information and POI information.</p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
Open the record for dataset details and reuse information.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Acacia constricta (whitethorn acacia) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Acacia constricta (whitethorn acacia) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Flourensia cernua (American tarwort) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Flourensia cernua (American tarwort) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Fouquieria splendens (ocotillo) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Fouquieria splendens (ocotillo) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Koeberlinia spinosa (crown of thorns) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Koeberlinia spinosa (crown of thorns) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Larrea tridentata (creosote bush) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Larrea tridentata (creosote bush) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Parthenium incanum (mariola) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Parthenium incanum (mariola) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Rhus microphylla (littleleaf sumac) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Rhus microphylla (littleleaf sumac) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of shrubs in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of shrubs measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Thymophylla acerosa (pricklyleaf dogweed) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Thymophylla acerosa (pricklyleaf dogweed) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Tiquilia canescens (woody crinklemat) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Tiquilia canescens (woody crinklemat) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Zinnia acerosa (desert zinnia) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Zinnia acerosa (desert zinnia) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant species richness in units of noper30pt5m on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant species richness measurements in noper30pt5m units and were aggregated to a yearly timescale.
INCLUSIVE DESIGN: PRINCIPLE-BASED CASE STUDIES OF THE NIGERIAN BUILT-UP SPACE
<p>Data set for Tables and figures for INCLUSIVE DESIGN: PRINCIPLE-BASED CASE STUDIES OF THE NIGERIAN BUILT-UP SPACE</p>
Built-up areas of Britain, 1790s-1830S
<p>This dataset includes the built up areas extracted from OS survey drawings, English county maps and Roy's military survey of Scotland.</p>
ScienceDex guides
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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.