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4,068 results for “geographic”
Cuneiform Inscriptions Geographical Site - Assemblage Estimates (CIGS-AE)
<p>The <em>Cuneiform Inscriptions Geographical Site - Assemblage Estimates </em>(CIGS-AE) provides basic overall estimates of and bibliographical references for the approximate number of cuneiform inscriptions derived from individual archaeological locations. In use across the wider Middle East from c. 3,400 BCE until 100 CE, cuneiform is one of the earliest and most extensively documented ancient scripts in world history. The CIGS-AE is a component of the <a href="https://doi.org/10.5281/zenodo.4960710">Cuneiform Inscriptions Geographial Site (CIGS)</a> index, a digital geospatial register of archaeological sites with finds of cuneiform inscriptions across Europe, Asia, and Africa.</p> <p>CIGS-AE provides a first comprehensive quantitative overview of the approximate number of cuneiform inscriptions unearthed from known archaeological locations. It does not provide an overview of the entire corpus of cuneiform inscriptions known, as the index disregards all inscriptions with no verifiable archaeological origin, estimated to be between fifteen and twenty per cent of the overall corpus according to the catalogue of the <a href="https://cdli.mpiwg-berlin.mpg.de/heatmap">Cuneiform Digital Library Initiative</a>. The present resource then offers a lower threshold for the size of the full cuneiform corpus and a fairly reliable overview of its general distribution. The accompanying bibliography offers a basic set of references for all known archaeological sites with finds of cuneiform inscriptions. This information is intended as a starting point for further study, and should not be considered an exhaustive nor authoritative bibliography.</p> <p>This resource has been prepared by researchers of the <a href="http://lingfil.uu.se/">Department of Linguistics and Philology</a> of <a href="http://uu.se/">Uppsala University</a>. The index is intended as a tool for students and researchers in cuneiform studies and related areas and as an aid to cultural heritage managers and educators in communicating and safeguarding this unique body of world written heritage. The index remains under development and is regularly updated. The authors will very much appreciate notices of any omissions, errors, or inaccuracies. For any inquiries, please contact <a href="https://www.katalog.uu.se/profile/?id=N18-1120">Rune Rattenborg</a> (<a href="mailto:rune.rattenborg@lingfil.uu.se?subject=%5BCIGS-AE%5D%3A">rune.rattenborg@lingfil.uu.se</a>).</p> <p>The version 1.1 index contains 603 entries with a total five fields, including one primary ID, two integer fields for assemblage size and grouping, and two string fields for bibliographical references and notes. Record identifiers are matched with primary IDs in <a href="https://doi.org/10.5281/zenodo.4960710">Cuneiform Inscriptions Geographial Site (CIGS)</a> index to allow for geospatial visualisation of quantitative data. Reference short titles are matched with short titles contained in the accompanying .bibtex.</p>
Dataset for: A continuous classification of the 480,000 lakes of the conterminous US based on geographic archetypes
<p>These datasets were used in a journal article with the goal of developing a new geographic classification approach for ~480,000 lakes ≥ 1 ha in the conterminous U.S. based on archetypes defined as endmembers with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. We identified seven lake archetypes; each study lake was then assigned weights for each of the archetypes. The data used to develop the archetypes, archetype weights, and variables used in associated analyses is provided in three data tables. The first includes the lake-specific transformed predictors used to generate the seven archetypes, the weights corresponding to each archetype, the archetype with the maximum weight and the weight of that maximum archetype. The second provides lake-specific raw values for each predictor and for the 19 response variables used to explore aspects of the archetype classification. The final metadata table provides a data dictionary for all columns in the previously mentioned data tables.</p>
Cuneiform Inscriptions Geographical Site Index (CIGS)
<p>The <em>Cuneiform Inscriptions Geographial Site</em> (CIGS) index contains a basic set of primary spatial, toponym, attribute, and external link information on close to 600 archaeological locations where texts written in cuneiform and derived scripts have been found. In use across the wider Middle East from c. 3,400 BCE until 100 CE, cuneiform is one of the earliest and most extensively documented ancient scripts in world history. This resource has been prepared by researchers of the Department of Linguistics and Philology of Uppsala University. The index is intended as a tool for students and researchers in cuneiform studies and related areas and as an aid to cultural heritage managers and educators in communicating and safeguarding this unique body of world written heritage. The index remains under development and is regularly updated. The authors will very much appreciate notices of any omissions, errors, or inaccuracies. For any inquiries, please contact <a href="https://www.katalog.uu.se/profile/?id=N18-1120">Rune Rattenborg</a> (<a href="mailto:rune.rattenborg@lingfil.uu.se">rune.rattenborg@lingfil.uu.se</a>). For further details, see <a href="https://cdli.ucla.edu/pubs/cdlj/2021/cdlj2021_001.html">Rattenborg et al. 2021</a>.</p> <p>The version 1.7 index contains 598 entries with a total twenty-six fields, including one primary ID, one integer field for accuracy, twenty-two string fields with toponyms and links, and two spatial data fields. Coordinates given use the WGS 1984 geographic coordinate reference system (<a href="https://epsg.io/4326">EPSG 4326</a>) and have been truncated to four decimal digits. Site locations have been traced from archaeological gazetteers and web mapping services (e.g. <a href="https://pleiades.stoa.org/">Pleiades</a>, <a href="https://www.geonames.org/">GeoNames</a> and <a href="https://www.openstreetmap.org/">OpenStreetMap</a>) and digitally generated from optical recognition using current and legacy satellite imagery datasets in QGIS 3.x.</p> <p>The version 1.7 data set is updated to correlate with archaeological locations included in the <a href="https://cdli.mpiwg-berlin.mpg.de">Cuneiform Digital Library Initiative</a> table of proveniences (see <a href="https://cdli.mpiwg-berlin.mpg.de/proveniences">https://cdli.mpiwg-berlin.mpg.de/proveniences</a>), migrated July 2023. Please see this resource for later updates to individual records.</p>
Database of Geographic Information: Change in groundwater level, Central Arizona-Phoenix, 1985-2000
Change in Level of Groundwater in central Arizona-Phoenix, 1985-2000. This file shows spatial changes in groundwater levels for two separate time periods: 1985-1989 and 1996-2000. This is a spatial data object with a Coordinate Reference System (CRS) of EPSG:3479 NAD83(NSRS2007) / Arizona Central (ft); https://www.spatialreference.org/ref/epsg/3479/). The coordinate reference system (CRS) associated with these data when they were constructed initially was misrepresented in early versions (<= knb-lter-cap.101.8) of this dataset. The CAP LTER has attempted to assign a CRS based on reasonable values but the accuracy of the identified CRS cannot be certain.
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Data from: Thermal niches of wheat curl mite, Aceria tosichella (Acari: Eriophyidae): congruence between physiological and geographical distribution data
<p><strong>Filename: parm.csv</strong></p> <p>Population growth rate of the two wheat curl mite (WCM) lineages reared in different temperatures.</p> <ol> <li>lineage - mitochondrial lineage (MT-1 or MT-8)</li> <li>temp - the rearing temperature (ºC)</li> <li>n - no. of replications of the experiment</li> <li>r, r.lower, r.upper - estimated intrinsic population growth rate and its 95% confidence intervals</li> </ol> <p><strong>Filename: aceria.csv</strong></p> <p>Data on field sampling localities, WCM abundance and thermal niche suitabiity.</p> <ol> <li>julian - Julian date</li> <li>x, y - geodetic coordinates (EPSG: 2180)</li> <li>stems - no. of stems collected</li> <li>MT1, MT8- mitochondrial lineage (MT-1 or MT-8)</li> <li>TNS1, TNS8 - thermal niche suitability for lineages</li> </ol>
Inter-Chemical Correlation results for the study: HHEARx2018-2537 (Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort.)
Title: Phthalates and childhood obesity in a racially, ethnically and geographically diverse cohort. <br>Species: Homo sapiens <br>Number of samples: 630 <br>Number of named analytes: 16 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=66 <br>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
Geographic coordinates, soil properties, plant species composition and vegetation survey data in the study on tidal marshes of the Ogeechee, Altamaha and Satilla estuaries in Georgia, USA
We examined patterns of habitat function (plant species richness), productivity (plant aboveground biomass and total C), and nutrient stocks (N and P in aboveground plant biomass and soil) in tidal marshes of the Satilla, Altamaha, and Ogeechee Estuaries in Georgia, USA. We worked at two sites within each salinity zone (fresh, brackish, and saline) in each estuary, sampling a transect from the creekbank to the marsh platform. Site-scale and plot-scale species richness decreased from fresh to saline sites. Standing crop biomass and total carbon stocks were greatest at brackish sites, followed by freshwater then saline sites.
Geographical variation in vegetative growth and sexual reproduction of the invasive Spartina alterniflora in China
We studied patterns in vegetative growth and sexual reproduction of introduced S. alterniflora at 22 sites at 11 geographic locations over a latitudinal gradient of ~2000 km from Tanggu (39.05 °N, high latitude) to Leizhou (20.90 °N, low latitude) in China. We further evaluated the basis of phenotypic differences by growing plants from across the range in a common garden for 2 growing seasons. We found distinct latitudinal clines in plant height, shoot density, and sexual reproduction across latitude. Some traits exhibited linear relationships with latitude; others exhibited hump-shaped relationships. We identified correlations between plant traits and abiotic conditions such as mean annual temperature, growing degree days, tidal range, and soil nitrogen content. However, geographic variation in all but one trait disappeared in the common garden, indicating that variation largely due to phenotypic plasticity. Only a slight tendency for latitudinal variation in seed set persisted for two years in the common garden, suggesting that plants may be evolving genetic clines for this trait. Note that these data were collected as part of a National Natural Science Foundation of China (NSFC) funded study led by Yihui Zhang in collaboration with GCE-LTER.
Contrasting plant adaptation strategies to latitude in the native and invasive range of Spartina alterniflora: geographic survey (2014) and Common garden (2015-2017)
We examined trait differences and evolution across geographic clines among continents of the intertidal grass Spartina alterniflora within its invasive and native ranges. Between September and November 2014, we sampled vegetative and reproductive traits in the field at 20 sites over 20° latitude in China (invasive range) and 28 sites over 17° latitude in the US (native range). We grew both Chinese and US plants in a greenhouse common garden for three years (2015 - 2017) to determine if differences in performance of S. alterniflora between the introduced and native ranges were due to genetic differences or differences in abiotic conditions.
PIE LTER, geographic information for the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA.
A description of the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA, USA. The marsh around Clubhead Creek, Rolwey, MA, USA was used as a reference.
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
Artificial COVID-19 Cases in Paris and Geographic Data Useful for Geomasking
<p>Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:</p> <ul> <li>roads_paris_IGN.shp contains the road lines from IGN France in Paris;</li> <li>buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);</li> <li>faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);</li> <li>ban_75.shp contains all the address points in Paris from the open BAN database.</li> <li>artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.</li> </ul> <p> </p>
Divergent geographic patterns and functional characteristics: Subtle mapping for ponds in the Yangtze River Delta Region
<p>Pond water surfaces (PWS) possess diverse functional types, such as aquaculture, agriculture-water supplement, and ecosystem regulation. However, existing research often treats PWS as a homogeneous aquatic ecosystem; the absence of a comprehensive PWS classification system hinders ours understanding of PWS background characteristics and is detrimental to watershed management. Here, a comprehensive classification system of PWS, including fish aquaculture ponds (FAP), shrimp and crab aquaculture ponds (SCAP), natural ponds (NP), and landscaping ponds (LP) was proposed from remote sensing perspectives. Additionally, interpretation rules were standardized from multi-features including spectrum, shape, topography, and surrounding geographical environments. Subsequently, refined spatiotemporal data product of PWS in the Yangtze River Delta from 2016 to 2022 was generated using Sentinel-2 images with 10 m spatial resolutions. The results indicate that: (1) The spatiotemporal changes exhibited three stages, i.e., “declining – stable – recovery.” The area of PWS decreased from 5186.52 km² to 4920.90 km²in 2016-2017, stabilized at approximately 4500 km² in 2019-2021, and then rebounded to 4834.12 km² in 2022. (2) Regarding different PWS functional types, significant differences were demonstrated in terms of area, surrounding environment, and spatiotemporal changes. Firstly, FAP dominated in terms of area, accounting for 47.81% of the total. Secondly, FAP was widely around rivers and lakes. At the same time, SCAP was concentrated around lakes or along the coast, LP was primarily found in urban areas, and NP was predominantly found in rural areas and mountainous regions; Thirdly, NP decreased as land remediation work progressed continuously, while LP increased due to policy support for urban renewal. Changes in aquaculture were more complex, experiencing a sharp decline from 2016 to 2020 due to reduced market demand but rebounded in 2021-2022 with supportive policies. In summary, the system and data products developed in this study reveal the diverse relationships of "functional type-geographical environment-driving factor" regarding PWS, implicating appropriate planning for aquatic ecosystem.</p>
Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions
<p><strong>Introduction</strong></p> <p>Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the <strong>Pan-Tibetan Highlands </strong>(new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the <strong>Tibetan Plateau</strong> and three adjacent mountain regions: the <strong>Himalaya</strong>, <strong>Hengduan Mountains</strong> and <strong>Mountains of Central Asia</strong>, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.</p> <p>The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:</p> <p>(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.</p> <p>(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.</p> <p>(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.</p> <p>Note: <strong>High Mountain Asia</strong>: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. <strong>Third Pole</strong>: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ <em>sensu lato</em> defined by some previous studies, but is more specific.</p> <p>More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.</p> <p> </p> <p><strong>Data processing</strong></p> <p>We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the <em>Layer to KML</em> tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the <em>shapefile</em> function of the raster package, and wrote it as a geojson file using the <em>geojson_write</em> function in the geojsonio package.</p> <p> </p> <p><strong>Version</strong></p> <p>Version 2022.1.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.</p> <p> </p> <p><strong>Citation</strong></p> <p>Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision</p> <p> </p> <p>Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940</p> <p> </p> <p><strong>Contacts</strong></p> <p>Dr. Jie LIU: E-mail: <a>liujie@mail.kib.ac.cn</a>;</p> <p>Mr. Guangfu ZHU: <a>zhuguangfu@mail.kib.ac.cn</a></p> <p>Institution: Kunming Institute of Botany, Chinese Academy of Sciences</p> <p>Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China</p> <p> </p> <p><strong>Copyright</strong></p> <p>This dataset is available under the Attribution-ShareAlike 4.0 International (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>).</p>
Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße für Deutschland - Geographical hexagonal grid with one square kilometer cell size for Germany
<p>Ein räumliches Gitter stellt eine abstrakte Definition von einheitlich großen Bezugszellen für statistische Auswertungen dar. Dieses macht Auswertungen und Vergleiche gegenüber räumlichen Abgrenzungen auf der Basis von administrativen Grenzen wie Kreisen oder Gemeinden unterschiedlicher Größen einfacher. Geografische Gitterdefinitionen werden eingesetzt, um eine vordefinierte Raumbezugsstruktur zu haben mit einer einheitlicheren Verteilung von Zellen in der Fläche.</p> <p><br> Ein Gitter mit sechseckigen, eben hexagonalen Zellen bietet eine Alternative zu rechteckigen statistischen Gittern wie das GeoGitter vom Bundesamt für Kartographie und Geodäsie (BKG, 2020). Ein Hexagon-Gitter bietet neben der Eigenschaft homogene Einheiten für statistische Analysen zu bilden (Schindler et al. 2008) mit sechs unmittelbaren Nachbarzellen vorteilhaftere Voraussetzungen für Nachbarschaftsanalysen im Vergleich zu quadratischen Zellen mit vier Kantennachbarn (White et al. 1992). Als Beispiel werden im Bild 1 mittels Hexagonen-Gitter Volumenunterschiede der aufragenden Vegetation pro Zelle dargestellt.</p>
Soil pH, developmental stages and geographical origin differently influence the root metabolomic diversity and root-related microbial diversity of Echium vulgare from native habitats
<p>R Studio codes and ASV table used to analyze the microbiome data of our Echium vulgare microbial ecology experiment. </p>
Dataset and additional figures for: decomposing geographical and universal aspects of human mobility
<p>Data and additional figures for: <em>Decomposing geographical and universal aspects of human mobility, </em><a href="https://arxiv.org/pdf/2405.08746">https://arxiv.org/pdf/2405.08746</a></p>
Geographic variation of tree height of Pinus pinea L. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinea </em>L. planted in common gardens in France and Spain, between years 1993 and 1997. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimensions of this database is 56,624 individual tree height measurements <em> </em>with 9 common gardens and 55 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.