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96 results for “geographic region”
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>
1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate
<p>1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.</p> <p> </p> <p>How to use</p> <pre>from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5))</pre> <p> </p> <p>Files:</p> <ul> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/towns.csv">towns.csv</a> - city information</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/regions.csv">regions.csv</a> - list of Russian Federation regions</li> <li><a href="https://github.com/epogrebnyak/ru-cities/blob/main/assets/alt_city_names.json">alt_city_names.json</a> - alternative city names</li> </ul> <p> </p> <p>Сolumns (towns.csv):</p> <p>Basic info:</p> <ul> <li><code>city</code> - city name (several cities have alternative names marked in <code>alt_city_names.json</code>)</li> <li><code>population</code> - city population, thousand people, Rosstat estimate as of 1.1.2020</li> <li><code>lat,lon</code> - city geographic coordinates</li> </ul> <p>Region:</p> <ul> <li><code>region_name</code> - subnational region (oblast, republic, krai or AO)</li> <li><code>region_iso_code</code> - <a href="https://en.wikipedia.org/wiki/ISO_3166-2:RU">ISO 3166 code</a>, eg <code>RU-VLD</code></li> <li><code>federal_district</code>, eg <code>Центральный</code></li> </ul> <p>City codes:</p> <ul> <li><code>okato</code></li> <li><code>oktmo</code></li> <li><code>fias_id</code></li> <li><code>kladr_id</code></li> </ul> <p> </p> <p>Data sources</p> <ul> <li>City list and city population collected from Rosstat publication <a href="https://rosstat.gov.ru/folder/210/document/13206">Регионы России. Основные социально-экономические показатели городов</a> and parsed from publication Microsoft Word files.</li> <li>City list corresponds to <a href="https://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%B3%D0%BE%D1%80%D0%BE%D0%B4%D0%BE%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8">this Wikipedia article</a>.</li> <li>Alternative dataset is <a href="https://github.com/hflabs/city">wiki-based Dadata city dataset</a> (no population data).</li> </ul> <p> </p> <p>Comments</p> <p> </p> <p>City groups</p> <ul> <li> <p><code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication as parts of <code>Тюменская область</code>.</p> </li> <li> <p>Several notable towns are classified as administrative part of larger cities (<code>Сестрорецк</code> is a municpality at Saint-Petersburg, <code>Щербинка</code> part of Moscow). They are not and not reported in this dataset.</p> </li> </ul> <p> </p> <p>By individual city</p> <ul> <li><code>Белоозерский</code> not found in Rosstat publication, but <a href="https://github.com/epogrebnyak/ru-cities/issues/5#issuecomment-886179980">should be considered a city as of 1.1.2020</a></li> </ul> <p> </p> <p>Alternative city names</p> <ul> <li> <p>We suppressed letter "ё" <code>city</code> columns in towns.csv - we have <code>Орел</code>, but not <code>Орёл</code>. This affected:</p> <ul> <li><code>Белоозёрский</code></li> <li><code>Королёв</code></li> <li><code>Ликино-Дулёво</code></li> <li><code>Озёры</code></li> <li><code>Щёлково</code></li> <li><code>Орёл</code></li> </ul> </li> <li> <p><code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city.</p> </li> </ul> <p><code>assets/alt_city_names.json</code> contains these names.</p> <p> </p> <p>Tests</p> <pre><code>poetry install poetry run python -m pytest </code></pre> <p> </p> <p>How to replicate dataset</p> <p> </p> <p>1. Base dataset</p> <p>Run:</p> <ul> <li>download data stro rar/get.sh</li> <li>convert <code>Саратовская область.doc</code> to docx</li> <li>run make.py</li> </ul> <p>Creates:</p> <ul> <li><code>_towns.csv</code></li> <li><code>assets/regions.csv</code></li> </ul> <p> </p> <p>2. API calls</p> <p>Note: do not attempt if you do not have to - this runs a while and loads third-party API access.</p> <p>You have the resulting files in repo, so probably does not need to these scripts.</p> <p>Run:</p> <ul> <li><code>cd geocoding</code></li> <li>run coord_dadata.py (needs token)</li> <li>run coord_osm.py</li> </ul> <p>Creates:</p> <ul> <li>coord_dadata.csv</li> <li>coord_osm.csv</li> </ul> <p> </p> <p>3. Merge data</p> <p>Run:</p> <ul> <li>run merge.py</li> </ul> <p>Creates:</p> <ul> <li>assets/towns.csv</li> </ul> <p> </p>
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
<p>Datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.</p> <p>Data processed from National Statistical System (INEI) publications.</p> <p>2025.</p>
LauNuts: A Knowledge Graph to identify and compare geographic regions in the European Union
<p><strong>LauNuts</strong> is a RDF Knowledge Graph consisting of:</p> <ul> <li>Local Administrative Units (LAU) and</li> <li>Nomenclature of Territorial Units for Statistics (NUTS)</li> </ul> <p><a href="https://w3id.org/launuts">https://w3id.org/launuts</a></p>
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America
<p><span>Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee<em> </em>assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by <span>modeling </span>species-specific habitat requirements, and assemblage-level responses across geographic regions. <span>We integrated species richness, abundance, and distribution data for 18 bumble bee species with site-specific bioclimatic, landscape composition, and landscape configuration data to evaluate</span> the effects of multiple environmental stressors <span>on bumble bee assemblages throughout</span> 433 agricultural fields in<span> Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east vs. west groupings emerged when evaluating species-specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact in the mountain region. Further, </span>forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages<span>. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. </span>Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the USA. </span></p>
Figure 8 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 8. Effects of experimental site by burial depth by year on percentage seed viability for Amoronthus polmeri and Amoronthus tuberculotus. Vertical bars represent ± standard error of the mean.
Figure 4 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 4. Monthly soil temperature (averaged over a 15-min concurrent recording period on a daily basis for the entire experimental period) at the top and at 15 cm below the soil surface (left y-axis) and monthly soil volumetric water content at 15 cm below soil surface (right y-axis) for each of the seven sites where the seed material of Amoronthus polmeri and Amoronthus tuberculotus was exposed to burial conditions during 2014–2016. Data presented for Missouri include only two experimental years (2015 and 2016).
Figure 1 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 1. Experimental sites across Midsouth United States, where Amoronthus polmeri and Amoronthus tuberculotus seed material was exposed to burial trials for a period of 1 to 3 yr before viability test evaluation. Numbers in parentheses represent the latitude and longitude of the experimental sites.
Figure 6 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 6. Interaction of experimental site by burial depth on percentage seed damage and loss averaged across 2014–2016 for Amoronthus polmeri and Amoronthus tuberculotus. Damaged seeds include broken, shrunk, or malformed seeds and those lost due to deterioration or futile germination, which were impossible to count. Vertical bars represent ± standard error of the mean. Supplemental information is also provided in Supplementary Tables S1 and S2, where the actual values averaged across three replications per treatment and five locations of seed material origins are shown.
Figure 3 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 3. Amoronthus polmeri seeds as they appeared under a dissection microscope (A) before the seed retrieval and cleaning processes and (B) after the cleaning process. The same criteria were used for A. tuberculotus seeds.
Figure 7 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 7. Percentage seed viability as affected by burial depth and retrieval time (in months) for Amoronthus polmeri and Amoronthus tuberculotus. Vertical bars represent ± standard error of the mean (i.e., 0.612 and 0.649 for A. polmeri and A. tuberculotus, respectively).
Figure 2 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 2. Experimental layout in which the randomized arrangement of main plots (i.e., retrieval year), subplots (colored seed bags representing the site by ecotype treatments), and sub-subplot (i.e., burial depth treatment) are depicted along with details for seedbank establishment (dimensions and burial depth of PVC cage).
Figure 5 in Seedbank Persistence of Palmer Amaranth (Amoronthus polmeri) and Waterhemp (Amoronthus tuberculotus) across Diverse Geographical Regions in the United States
Figure 5. Interaction of experimental site by ecotype on seed viability for Amoronthus polmeri and Amoronthus tuberculotus averaged across 2014–2016. Vertical bars represent ± standard error of the mean. Supplemental information is also provided in Supplementary Tables S1 and S2, where the actual values averaged across three replications per treatment and five locations of seed material origins are shown.
Figs. 4A-B in New record and expansion of the geographic distribution of Parodia mueller-melchersii (Frič ex BAckeb.) N. P. TAylor (CActAceAe) in the Southern region of Rio GrAnde do Sul, BrAZil
Figs. 4A-B. Parodia mueller-melchersii habitat in Jaguarão, RS. A. Rocky outcrop; B. Surrounding vegetation.
Figs. 2A-D. Parodia mueller-melchersii.A in New record and expansion of the geographic distribution of Parodia mueller-melchersii (Frič ex BAckeb.) N. P. TAylor (CActAceAe) in the Southern region of Rio GrAnde do Sul, BrAZil
Figs. 2A-D. Parodia mueller-melchersii.A. General appearance (frontal view); B. General appearance (lateral view); C. Habitus; D. Juvenile specimens.
Figures 14–17 in The geographic pattern of distribution of the genus Rhopalurus Thorell, 1876 in the Guayana-Amazon region (Scorpiones: Buthidae)
Figures 14–17: Rhopalurus pintoi kourouensis ssp. n. 14. Carapace. 15. Metasoma and telson, lateral aspect. 16–17. Metasoma and telson, dorsal and ventral aspects.
Figures 10–13 in The geographic pattern of distribution of the genus Rhopalurus Thorell, 1876 in the Guayana-Amazon region (Scorpiones: Buthidae)
Figures 10–13: Rhopalurus pintoi kourouensis ssp. n. 10. Chela, dorso-external aspect, showing trichobothria and very intense setation. 11. Chela, idem, represented without setation. 12–13. Patella and femur, dorsal aspect. Again an important setation can be observed.
Figures 5–9 in The geographic pattern of distribution of the genus Rhopalurus Thorell, 1876 in the Guayana-Amazon region (Scorpiones: Buthidae)
Figures 5–9: Rhopalurus crassicauda paruensis ssp. n., male holotype. 5–7. Chela, dorso-external, ventral and internal aspects, showing trichobothria. 8–9. Tarsi of leg IV, lateral and ventral aspects, showing setation.
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.