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Habitatquarries: distribution of underground marl quarries in the Flemish Region and border areas, with the Flemish distribution of Natura 2000 habitat type 8310
<p><strong>General</strong></p> <p>The data source is a geospatial collection of polygons that correspond with the presence or absence of the Natura 2000 Annex I habitat type 8310 (Caves not open to the public) in the Flemish Region (and border areas), Belgium. </p> <p>The dataset contains all known, not collapsed, underground marl quarries in Flanders. Several of these quarries have their entrance in or run underground to the neighboring regions/countries.</p> <p>In general, different polygons represent different quarry units with their own internal climatic environment. Units that cross Flemish borders have been split into separate polygons. Exceptionally they may overlap if such units are situated above each other. </p> <p>For safety reasons, the dataset only contains the contour of the quarries, and no details like floor plans or entrances. For admission to research the indoor climate, please contact the Quarries and Safety Department of the municipality of Riemst (<a href="https://www.riemst.be/nl/wonen/groeven">https://www.riemst.be/nl/wonen/groeven</a>; <a href="mailto:mike.lahaye@riemst.be">mike.lahaye@riemst.be</a>).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, a scientific institute of the Flemish government).</p> <p> </p> <p><strong>Technical aspects</strong></p> <p>The data source is a GeoPackage that contains:</p> <ul> <li> <p>a spatial polygon layer ‘<code>habitatquarries</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p>a non-spatial table ‘<code>extra_references</code>’ with site-specific bibliographic references.</p> </li> </ul> <p>The data source has been based on an unpublished shapefile used in De Saeger & Lahaye (2019) and on a BibTeX bibliography file. See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/c0821eb/src/generate_habitatquarries">'n2khab-preprocessing' at commit c0821eb</a> for the creation.</p> <p>A reading function to return <code>habitatquarries</code> (this data source) in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The attributes of the spatial polygon layer ‘<code>habitatquarries</code>’ are: </p> <ul> <li> <p><code>polygon_id</code>: a unique number per polygon; </p> </li> <li> <p><code>unit_id</code>: a unique number for each quarry unit. Quarry units consisting of several polygons (= partly outside the Flemish region) have a number greater than 100;</p> </li> <li> <p><code>name</code>: name of the site;</p> </li> <li> <p><code>habitattype</code>: either:</p> <ul> <li> <p><code>8310</code> (habitat type 8310)</p> </li> <li> <p><code>gh</code> (no Natura 2000 type)</p> </li> <li> <p>missing (outside of the Flemish Region);</p> </li> </ul> </li> <li> <p><code>extra_reference</code>: extra reference with more information.</p> </li> </ul> <p>The non-spatial table <code>extra_references</code> provides the bibliography referred to by the spatial attribute <code>extra_reference</code>. It was derived from a BibTeX bibliography file by using the R-package <a href="https://docs.ropensci.org/bib2df">bib2df</a>, and it is back-convertible into one (see R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>). The original bibliography file is also available in the above linked ‘n2khab-preprocessing’ repository.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Hourly Mean Radiant Temperature Distribution on a summer day (2012-06-27) in the greater Phoenix, Arizona (USA) metropolitan area
This dataset is a collection of 1-m resolution mean radiant temperature (Tmrt) rasters generated using digital surface models and the Solar and longwave environmental irradiance geometry (SOLWEIG) model. The dataset provides hourly Tmrt in the Phoenix, Arizona (USA) metropolitan area for a typical summer day (June 27, 2012, peak air temperature of 41 degrees C) from 07:00 hrs to 20:00 hrs (local time (America/Phoenix)). The dataset can serve as a guide for heat mitigation programs within the area and as input for heat exposure studies.
Figure 2. Summer core area delineation. The straight line with a in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 2. Summer core area delineation. The straight line with a slope of –1 represents the random use of space within the population seasonal range. The curve that sags below the line of random use represents the clumped use of space. The summer core area can be defined at the point whose tangent has slope –1, e.g. 85%, that is, whose tangent is parallel to the line of random use. This is also the point of the curve that is furthest from the line of random use.
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
Spatiotemporal distribution of global peatland area during the Holocene
<p>The global peatland area dataset comprises netCDF files, which consist of 13 sets of maps showing the global extent of peatlands at a spatial resolution of 0.5° × 0.5°. All maps are provided at 1,000-year time intervals between 12 and 0 ka BP. The peatland area files named “Global_peatland_area_BA_*” were reconstructed using the BA method, and the files named “Global_peatland_area_IDW_*” were reconstructed using the IDW method. The global peatland records included data on location, latitude, longitude, peat type, basal ages, and end ages. The global pollen of <em>Sphagnum</em> records included latitude, longitude,<em> Sphagnum</em> content, and peat basal and end ages.</p>
Porosity distribution in sub-skin boundary area of the powderbed additively manufactured parts
<p>Repository contains measurement results of the experimental investigationn of the sub-skin porosity in additively manufactured parts. Specimens were manufactured using machine manufacturer's suggested process parameters. Four different machines (EOS M400, TRUMPF TruPrint 1000, SLM 280 and DMG MORI LASERTEC 30 2nd gen.) and four different powder materials (mararging steel 1.2709, aluminum alloy AlSi10Mg, Titanium grade 5 and stainless steel 1.4404) are covered. Influences of the relative orientation of the hatch and boundary scanning tracks was investigated. Efficiency of the mitigation strategy againts sub-skin porosity issues through distance variation between hatch and boundary tracks was evaluated.</p> <p>Please refer to README.MD (or .PDF) for more detailed information about this dataset.</p>
Dasineura oxycoccana, the blueberry gall midge distribution, and cultivation areas of blueberry in South Korea
<p>Distribution data of Dasineura oxycoccana for species distribution modeling and cultivation areas of blueberry in South Korea</p> <p> </p>
Text-fig. 2. Distribution of different rock units in the turbidite facies in Majalengka area, West Java, Indonesia. in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 2. Distribution of different rock units in the turbidite facies in Majalengka area, West Java, Indonesia.
Text-fig. 3. Outcrop cross section of the turbidite facies distribution in the Majalengka, correlated northwest to southeast. The progradation pattern indicated by thickening of sandstone into the basin area are shown. F1 – heterolithic sandstone-mudstone 1; F2 – heterolithic sandstone-mudstone 2; F3 – mudstone facies; F4 – heterolithic fine sand and mudstone; F5 – conglomeratic to massive sandstone facies (Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 3. Outcrop cross section of the turbidite facies distribution in the Majalengka, correlated northwest to southeast. The progradation pattern indicated by thickening of sandstone into the basin area are shown. F1 – heterolithic sandstone-mudstone 1; F2 – heterolithic sandstone-mudstone 2; F3 – mudstone facies; F4 – heterolithic fine sand and mudstone; F5 – conglomeratic to massive sandstone facies (Muljana 2012).
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).
Measuring protected-area effectiveness using vertebrate distributions from leech iDNA
<p>Protected areas are key to meeting biodiversity conservation goals, but direct measures of effectiveness have proven difficult to obtain. We address this challenge by using environmental DNA from leech-ingested bloodmeals to estimate spatially-resolved vertebrate occupancies across the 677 km<sup>2</sup> Ailaoshan reserve in Yunnan, China. From 30,468 leeches collected by 163 park rangers across 172 patrol areas, we identify 86 vertebrate species, including amphibians, mammals, birds and squamates. Multi-species occupancy modelling shows that species richness increases with elevation and distance to reserve edge. Most large mammals (e.g. sambar, black bear, serow, tufted deer) follow this pattern; the exceptions are the three domestic mammal species (cows, sheep, goats) and muntjak deer, which are more common at lower elevations. Vertebrate occupancies are a direct measure of conservation outcomes that can help guide protected-area management and improve the contributions that protected areas make towards global biodiversity goals. Here, we show the feasibility of using invertebrate-derived DNA to estimate spatially-resolved vertebrate occupancies across entire protected areas.</p>
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations.
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village in A new record of the northern birch mouse Sicista betulina (Pallas, 1779) in the north of the Arkhangelsk Region (Rodentia: Sminthidae)
Рис. 1. Sicista betulina: 1 — Λесная мышовка, пойманная в окрестностях сеΛа КойÀа; 2 — карта распространения виÀа: зеΛеная заΛивка — ареаΛ виÀа по Burgin et al. 2020, синие звезÀочки — точки нахоÀок в национаΛьном парке «Онежское Поморье» и на СоΛовецком архипеΛаге (Черенкова 2014), красный круг — новая нахоÀка в районе Àеревни КойÀа; 3–4 — биотопы, в которых быΛ встречен виÀ в окрестностях сеΛа КойÀа Fig. 1. Sicista betulina: 1 — the northern birch mouse caught in the vicinity of Koida village; 2 — the species distribution map: the green fill — species range according to Burgin et al. 2020; the blue stars — points where the species was found in the Onezhskoye Pomorye National Park and on the Solovetsky Archipelago (Cherenkova 2014); the red circle — a new find in the area of Koida village; 3–4 — biotopes in which the species was encountered in the vicinity of Koida village
Fig. 4 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 4. Haplotype network showing the occurrence of three groups (upper rio Xingu, upper rio Paraguay and upper rio Tapajós). Traces show the number of mutational steps from two adjacent haplotypes. Circle diameters are proportional to the number of individuals, which each haplotype and the colors represent the locality were those haplotypes were found. Upper rio Xingu= Pink (1: dark pink); upper rio Paraguay = Blue (2: light blue; 3: navy blue; 4: dark blue; 5: light pink; 6: orange; 7: light purple; 8: dark purple; 9: white; 10: yellow; 11: light green; 12: dark green); and upper rio Tapajós= Gray (13: light gray and 14: dark gray).
Fig. 3 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 3. Phylogenetic tree showing relationships among major lineages of Jupiaba acanthogaster from the upper rio Paraguay, upper rio Tapajós and upper rio Xingu, obtained by a maximum likelihood partitioned analysis. Numbers at each of the main nodes represents percentage of bootstrap support obtained by maximum parsimony analysis (1000 bootstrap pseudoreplicates).
Fig. 2 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 2. Distribution of sampled localities for Jupiaba acanthogaster in the upper rio Paraguay, rio Tapajós and rio Xingú basins. The drainages of the rio Tocantins, rio Araguaia and upper rio Paraná are also illustrated. Drainage boundaries delimited by a continuous black line.
Fig. 1 in Distributions and phylogeographic data of rheophilic freshwater fishes provide evidences on the geographic extension of a central-brazilian amazonian palaeoplateau in the area of the present day Pantanal Wetland
Fig. 1. Map of the upper rio Paraguay basin and adjoining areas showing the distribution of Leporinus octomatulatus, Jubiaba acanthogaster, Oligosarcus perdido, Moenkhausia cosmops, and Hypostomus cochliodon, exemplifying distributional pattern discussed in this paper.
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.