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Presence locations (direct and indirect) of blue sheep and livestock in Johar Valley, Uttarakhand, India from years 2015-2017
<p><span>In-depth knowledge of distribution and their factors are important for species conservation and management. Many forms of such data have led to development of new analytical techniques for better interpretation. For mountainous terrains with certain limitations, species data is obtained in presence-only form. Point process model is one of the recent approaches for modelling such data, taking care of pseudo-absences and spatial independence. For conservation of regions with limited resources and species with similar ecological requirements, it is important to properly assess competition extent between wild and domestic species. We attempted to use point process framework to estimate resource selection function of blue sheep (<i>Pseudois nayaur</i>) in pastoralism influenced areas of a western Himalayan landscape. Our study is a first attempt to use this framework to estimate resource selection on a dataset not collected using radio-telemetry. Spatial locations of presence points of blue sheep and livestock and a background sample of random points along with six topographic covariates were used for modelling resource selection probability via intensity function. Blue sheep showed its predicted presence in areas with open vegetation coinciding with alpine meadows, influenced by southern aspect keeping a threshold distance of 600m-1000m from cliffs (escape terrain). Livestock, also showed presence probability in open vegetation but at lower altitudes mainly on valley floors. Our results suggest that though blue sheep continued to use the same habitat type after livestock arrival, they selected different resources based on topographic factors. Livestock were in areas convenient for pastoralists for establishing their campsites and feasibility of grazing along with presence of nutritious grasses. Thus, we assume that probable shift of habitat for blue sheep from optimal areas occurs due to livestock presence, which might disturb their nutritional balance. Our study provides helpful insights for management of rangelands, which when tied with dietary patterns will give wholesome idea for proper conservation measures in future.</span></p>
FIGURE 5 in Bromus husainii (Poaceae:Bromeae), a new species from Valley of Flowers National Park, Uttarakhand, India
FIGURE 5. Bromus husainii growing in its natural habitat.
FIGURE 4. Bromus husainii. A. Nodes. B. Collar. C in Bromus husainii (Poaceae:Bromeae), a new species from Valley of Flowers National Park, Uttarakhand, India
FIGURE 4. Bromus husainii. A. Nodes. B. Collar. C. Ligule.
FIG. 8 in The Early Devonian Acanthodian Uraniacanthus curtus (Powrie, 1870) n. comb. from the Midland Valley of Scotland
FIG. 8. — Uraniacanthus curtus (Powrie, 1870) n. comb., NMS G.1891.92.249, holotype from Farnell: Magnified impressions of the body scales. Scale bar: 1 mm.
FIGURE 4 in Anemone pindariensis sp. nov., a new species from Pindari valley of the Western Himalaya, India
FIGURE 4. Anemone pindariensis (Holotype)
FIGURE 2. Anemone pindariensis Harsh Singh & S.K in Anemone pindariensis sp. nov., a new species from Pindari valley of the Western Himalaya, India
FIGURE 2. Anemone pindariensis Harsh Singh & S.K. Barik: A. Habitat; B and C. Flower
FIGURE 1 in Anemone pindariensis sp. nov., a new species from Pindari valley of the Western Himalaya, India
FIGURE 1. Location map of Anemone pindariensis in Uttarakhand.
Fig. 1 in Assessing large mammal and bird richness from camera-trap records in the Hukaung Valley of Northern Myanmar
Fig. 1. Location of Hukaung Valley Wildlife Sanctuary and Core study area (hatched) in Northern Myanmar.
Fig. 1 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil
Fig. 1. Study region, indicating the ten sampled lakes. The lakes Capim (Ca), Ferrugem (Fe) and Nova (No) have only native fish species, lakes Crentes (Cr), Poço Redondo (Po), Romoalda (Ro), and Timburé (Ti) have non-piscivorous non-native fish species present, and lakes Águas Claras (Ag), Ariranha (Ar), and Palmeirinha (Pa) have non-native piscivores present.
Causes of differences in the distribution of the invasive plants Ambrosia artemisiifolia and Ambrosia trifida in Yili Valley, China
<p class="1"><i>Ambrosia artemisiifolia</i> and <i>Ambrosia trifida</i> are two species of very harmful and invasive plants of the same genus. However, it remains unclear why <i>A. artemisiifolia </i>is more widely distributed than <i>A. trifida</i> worldwide.</p> <p class="1">Distribution and abundance of these two species were surveyed and measured from 2010 to 2017 in the Yili Valley, Xinjiang, China. Soil temperature and humidity, main companion species, the biological characteristics in farmland ecotone, residential area, roadside and grassland, and water demand of the two species were determined and studied from 2017 to 2018.</p> <p class="1">The area occupied by <i>A. artemisiifolia </i>in the Yili Valley was more extensive than that of <i>A. trifida</i>, while the abundance of <i>A. artemisiifolia </i>in grassland was less than that of <i>A. trifida</i> at eight years after invasion. The interspecific competitive ability of two species were stronger than those of companion species in farmland ecotone, residential, and roadside. In addition, <i>A. trifida </i>had greater interspecific competitive ability than other plant species in grassland. The seed size and seed weight of <i>A. trifida</i> were five times or eight times those of <i>A.artemisiifolia</i>. When comparing the changes under simulated annual precipitation of 840 mm versus 280 mm, the seed yield per m<sup>2</sup> of <i>A. trifida</i> decreased from 50,185 to 19, while that of <i>A. artemisiifolia </i>decreased from 15,579 to 530.</p> <p>The differences in the distribution of the two species are mainly due to differences in interspecific competitive ability, seed size, and water dependence. The two species have stronger interspecific competitive ability than that of companion species, but <i>A. artemisiifolia </i>has a smaller seed size and stronger drought tolerance, which allows <i>A. artemisiifolia </i>to spread farther than <i>A. trifida</i>. The reason for wider distribution of <i>A. trifida</i> in grassland is that <i>A. trifida</i> has stronger interspecific competitive ability than <i>A. artemisiifolia </i>under sufficient water.</p>
The Colorado River Delta and California's Central Valley are critical regions for many migrating North American landbirds
<p>Migration is an important component of some species full annual cycle. California's Central Valley and the Colorado River Delta provide important riparian and wetland habitats for migrating waterbirds in the arid west of North America, but little is known about whether these locations are important at the population level to migrating landbirds. We used eBird Status and Trends abundance data to quantify the importance of the Central Valley and Colorado River Delta to landbirds by estimating the proportion of the breeding population of 112 species that use each site during migration. We found that ~17 million landbirds use the Colorado River Delta in the spring and ~14 million in the fall. Across four study regions in the Central Valley, up to ~65 million landbirds migrate through in the spring and up to ~48 million in the fall. In the spring and fall, respectively, up to 37 and up to 30 species had at least 1% of their continental population migrate through the study regions. We also quantified the spatial concentration of each species across latitudinal transects to determine the extent to which study regions were acting as migratory bottlenecks. Landbird abundances were spatially concentrated in study regions 29.4% of all migration weeks, indicating that each study region acts as a migratory bottleneck. This application of eBird data is a powerful approach to quantifying the importance of sites to migrating birds. Our results provide evidence of population-level importance of the Central Valley and Colorado River Delta for many migratory landbirds.</p>
Figure 1 from: Leśniewska M, Jastrzębski P, Stańska M, Hajdamowicz I (2015) Centipede (Chilopoda) richness and diversity in the Bug River valley (Eastern Poland). In: Tuf IH, Tajovský K (Eds) Proceedings of the 16th International Congress of Myriapodology, Olomouc, Czech Republic. ZooKeys 510: 125-139. https://doi.org/10.3897/zookeys.510.8763
Figure 1 - Location of the study area in Poland. Study area. Morzyczyn, Płatkownica – lower section of the Bug valley: Mogielnica, Zabuże, Gnojno – central section of the Bug valley.
Figure 3 from: Leśniewska M, Jastrzębski P, Stańska M, Hajdamowicz I (2015) Centipede (Chilopoda) richness and diversity in the Bug River valley (Eastern Poland). In: Tuf IH, Tajovský K (Eds) Proceedings of the 16th International Congress of Myriapodology, Olomouc, Czech Republic. ZooKeys 510: 125-139. https://doi.org/10.3897/zookeys.510.8763
Figure 3 - Dendrogram of the similarities of centipede composition in different habitats (distance/similarity measure – Bray and Curtis, cluster method –nearest neighbor) For designations see Table 1.
Figure 2 from: Leśniewska M, Jastrzębski P, Stańska M, Hajdamowicz I (2015) Centipede (Chilopoda) richness and diversity in the Bug River valley (Eastern Poland). In: Tuf IH, Tajovský K (Eds) Proceedings of the 16th International Congress of Myriapodology, Olomouc, Czech Republic. ZooKeys 510: 125-139. https://doi.org/10.3897/zookeys.510.8763
Figure 2 - Similarity of species (distance/similarity measure Bray and Curtis; cluster method: nearest neighbor). Species nomenclature in Table 1.
FIGURE 1. A in Schoenorchis mishmensis sp. nov. (Orchidaceae) from Mishmi Hills Lower Dibang Valley District, Arunachal Pradesh, India
FIGURE 1. A. Natural habit of Schoenorchis mishmensis. Photos by K. Gogoi.
Photograph dataset - Participant observation - Digbeth (Birmingham), St. George´s Cultural Quarter (Leicester), Ouseburn Valley (Newcastle upon Tyne) - United Kingdom
<p>Photographs taken during various participant observations developed between September 2016 and March 2020 by Jennifer García Carrizo.</p> <p>For further details, please consult:</p> <ul> <li>GARCÍA CARRIZO, J. (2022). Estrategias para la inclusión de personas con discapacidad. El caso del Ouseburn Valley (Newcastle upon Tyne, Reino Unido). <em>Prisma Social,</em> 37, 182–211. <a href="https://revistaprismasocial.es/article/view/4570/5314c">https://revistaprismasocial.es/article/view/4570/5314c</a> </li> <li>GARCÍA CARRIZO, J. (2022). Festivales y eventos como puntos de activación de la ciudadanía y de espacios culturales y creativos. In S. Liberal & G. Bonales (Coords.), <em>La representación de la ciudad en la sociedad contemporánea </em>(9-26). Mc Graw Hill. ISBN 978-84-486-4059-0</li> <li>GARCÍA CARRIZO, J. (2022). Empoderamiento, comunicación y activismo: el Ouseburn Trust como un catalizador de la participación ciudadana. In J.C. Figuereo-Benítez & R. Mancinas-Chávez (Coords.), <em>El poder de la comunicación: actores, estrategias y alternativas </em>(520-540). Dykinson. <a href="https://eprints.ucm.es/id/eprint/76022/"> https://eprints.ucm.es/id/eprint/76022/ </a></li> <li>GARCÍA CARRIZO, J. (2021). Regeneración urbana mediante industrias culturales y creativas. El caso de Digbeth (Reino Unido), un espacio para vivir, trabajar y disfrutar. <em>Arte y ciudad. Revista de Investigación</em>, 19, 121-152. <a href="https://doi.org/10.22530/ayc.2021.19.589">https://doi.org/10.22530/ayc.2021.19.589</a></li> <li>GARCÍA CARRIZO, J. (2021). El distrito cultural y creativo del Digbeth (Birmingham, Reino Unido) y sus planes de regeneración urbana. <em>Arte y Políticas de Identidad</em>, 25(25), 181–204. <a href="https://doi.org/10.6018/reapi.506261">doi.org/10.6018/reapi.506261</a> </li> <li>GARCIA CARRIZO, J. and GRANGER, R.C. (2020). Cultural and Creative Districts as Spaces for Value Change. In Granger, R.C. (ed.) <em>Value Construction in the Creative Economy. Negotiating Innovation and Transformation</em> (177-198). Basingstoke Springer International Publishing - Palgrave Macmillan. (https://doi.org/10.1007/978-3-030-37035-0). https://eprints.ucm.es/id/eprint/75633/ </li> <li>GARCÍA CARRIZO, J. (2019). La construcción de marca de distrito cultural y creativo del "Ouseburn Valley" (Newcastle Upon Tyne, Reino Unido): análisis de su documentación filmográfica. <em>Documentación de las Ciencias de la Información, </em>42, 19-37. <a href="https://doi.org/10.5209/dcin.64420">https://doi.org/10.5209/dcin.64420</a> </li> <li>GARCÍA CARRIZO, J. (2019). Distritos Culturales y Creativos como espacio para el activismo: análisis del caso del Ouseburn Valley en Newcastle upon Tyne. In JIVKOVA SEMOVA, D., ALADRO VICO, E. & POPELKA SOSA SÁNCHEZ, R. (Eds.), <em>Entender el Artivismo </em>(117-133)<em>.</em> Peter Lang. (ISBN: 978-1-78874-434-8).</li> <li>GARCÍA CARRIZO, J. (2018). Gestión de la marca distrito cultural y creativo. Análisis del Leicester´s Cultural Quarter y su notoriedad como marca-distrito cultural y creativo entre turistas. In CHAVES MARTÍN, M.A. & Tejeda Martín, I. (Eds.), <em>Distritos Culturales y Revitalización Urbana </em>(201-220)<em>.</em> Icono 14. <a href="http:// https://eprints.ucm.es/id/eprint/63528/">https://eprints.ucm.es/id/eprint/63528/</a></li> </ul>
Air temperature and thermal comfort data measured and modelled for 121 workplaces in the Upper Rhine Valley
<p>The uploaded files contain the measured and modelled indoor data at different workplaces in the Upper Rhine Valley between August 1, 2021, and July 31, 2022 presented in the article "Predicting Indoor Air Temperature and Thermal Comfort in Occupational Settings Using Weather Forecasts, Indoor Sensors, and Artificial Neural Networks" by Sulzer et al. (2023), <a href="http://doi.org/10.1016/j.buildenv.2023.110077">doi.org/10.1016/j.buildenv.2023.110077</a>. Information about the characteristics of each workplace can be found in the appendix of the article. For every workplace two files are uploaded, one for the data of the indoor air temperature (Ta) and one for indoor physiological equivalent temperature (PET). The workplace ID and variable are stated in the filename. The columns in the files contain the following data:</p> <ul> <li>"Datetime (UTC)": This column contains the timestamp in UTC of the starting point of the interval used for the one-hour mean values .</li> <li>"MoBiMet data": This column contains the one-hour mean values of Ta or PET of the data derived every five minutes by the low-cost Mobile Biometeorology System (MoBiMet) at the corresponding workplace in °C. The MoBiMet are presented in detail in <a href="http://doi.org/10.3390/s22051828">doi.org/10.3390/s22051828</a>.</li> <li>"Used for": This column contains the information if the data point was used for training of the models (t), evaluation of the models (e), or not used for model training or evaluation due to missing indoor observation data (n).</li> <li>"Product 0 (ICON_outdoor)": This column, in the files for the indoor air temperature, contains the ICON-D2 data of the air temperature 2m a.g.l. in °C of the grid cell in which the associated work station is located.</li> <li>"Product 0 (ICON_outdoor) air temperatur 2m (C) input for PET calculation using RayMan","Product 1 (ICON_outdoor) vapor pressure 2m (hPa) input for PET calculation using RayMan","Product 1 (ICON_outdoor) wind speed 10m (m/s) input for PET calculation using RayMan", and "Product 1 (ICON_outdoor) global radiation surface (W/m²) input for PET calculation using RayMan": This columns contain the data of the outdoor air temperature 2m a.g.l. in °C, the vapor pressure 2m a.g.l., derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located, which were used in RayMan Pro to calculate the PET for outdoors.</li> <li>"Product 2 (ANN_Generic)": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data as input. The generic data contain hourly solar altitude and azimuth at each location, the weekday, and a sine and cosine function of the daily and yearly cycle.</li> <li>"Product 3 (ANN_AWS) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed of an automated weather station in Freiburg (Station FRCHEM; 48°00’04’’ N; 7°50’55’’ E).</li> <li>"Product 3 (ANN_AWS) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the automated weather station as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 4 (ANN_ICON) without past data": This column contains the indoor data of PET or air temperature in °C modelled by an artificial neural network using generic data and the meteorological data of air temperature, vapor pressure, mean sea level pressure, global radiation, longwave downwelling radiation, and wind speed derived from the ICON-D2 weather forecast data of the grid cell in which the associated work station is located.</li> <li>"Product 4 (ANN_ICON) with past data": This column contains similar data than the column before but the artificial neural network models used "past data" of the ICON-D2 weather forecast data as additional input variables to model indoor air temperature and PET in °C. Additional to the hourly average of the meteorological data for each actual time (t), hourly averages for t-1 h, t-3 h, t-6 h, t-12 h, and t-24 h of air temperature, global radiation, and Longwave downwelling radiation are used as so called "past data".</li> <li>"Product 5 (ANN_Mixed) without past data": This column contains the indoor data of PET or air temperature in °C modelled by the same artificial neural network models as in Product 3 but applied for the same input data of Product 4.</li> <li>"Product 5 (ANN_Mixed) with past data": This column contains similar data than the column before but also takes into account the "past data".</li> </ul> <p>The data of Product 3 (ANN_AWS) and Product 5 (ANN_Mixed) are only available for locations in Freiburg, because the data of an automated weather station in Freiburg was used.<br> Data which is not available is stated as NA.</p>
FIGURE 10 in South America stygobiont crustaceans: a new species of Hyalellidae (Amphipoda) and new reports of Stygocarididae (Anaspidacea) and Protojaniridae (Isopoda) from Calingasta Valley, Pre-Andean region, San Juan, Argentina
FIGURE 10. Habitus (photograph) of Cuyojanira sp. (FML-CRUST 01290) (Isopoda, Protojaniridae).
Supplementary material 1 from: Pino Infante GE, Samain M-S, Albán Castillo JA, Alomía Collazos LEA (2023) Species of Peperomia (Piperaceae) from the Saña River Valley, Peru. PhytoKeys 225: 1-40. https://doi.org/10.3897/phytokeys.225.99277
Localities of Peperomia species from the Saña River Valley, Peru
FIGURE 4. Cicindela timbisha n in A new species of tiger beetle (Coleoptera: Cicindelidae) from the Death Valley ecosystem
FIGURE 4. Cicindela timbisha n. sp. in situ in dark muddy soil near a freshwater spring.
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.