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450 results for “Spatio-temporal”
FIGURES 24–25 in Resurgence of a forgotten Southern Brazil endemic species: taxonomic position, redescription, and spatio-temporal distribution of Porosagrotis carolia Schaus, 1929 (Lepidoptera: Noctuidae: Noctuinae)
FIGURES 24–25. Feltia carolia (Schaus, 1929) comb. nov., male wing shape and venation.
Dataset used in Spatio-temporal analysis of Dengue fever in Bolivia: Climatic, environmental and sociodemographic factors
<p>This is the dataset used for Spatio-temporal analysis of Dengue fever in Bolivia: Climatic, environmental and sociodemographic factors. It is entirely composed of data from public domain sources. These are:</p> <p>Niño index is from National Oceanic and Atmospheric Administration (NOAA). (2023, November 8). Oceanic Niño Index (ONI). El Niño/Southern Oscillation (ENSO). https://www.ncei.noaa.gov/access/monitoring/enso/sst</p> <p>Dengue statistics are from the Organización Panamericana de la Salud. (2023, October 23). Serotipos Dengue por año para los países y territorios de las Américas: Bolivia (2013-2023). PLISA Plataforma de Información en Salud para las Américas. https://www3.paho.org/data/index.php/es/temas/indicadores-dengue/dengue-nacional/549-dengue-serotypes-es.html</p> <p>NVDI data is from the Food and Agriculture Organisation of the United Nations. This data is licensed Creative Commons Attribution-NonCommercial-ShareAlike 4.0 and we have licensed our dataset with this licence so that we can distribute it as part of our dataset. The original URL is https://www.fao.org/giews/earthobservation/country/index.jsp?lang=en&code=BOLFAO.GRAPH_NDVI_AGRI.<br>License: CC BY-NC-SA 3.0 IGO. Extracted from: https://www.fao.org/giews/earthobservation/asis/data/country/BOL/GRAPH_NDVI_AGRI/ndvi_adm1_data.csv. Date of Access: 18-04-2024<br>The specific link to the data is https://www.fao.org/giews/earthobservation/asis/data/country/BOL/GRAPH_NDVI_AGRI/ndvi_adm1_data.csv</p> <p>Meterological data is from the Bolivian National Meteorological and Hydrological Service https://senamhi.gob.bo/index.php/inicio</p> <p> </p> <p> </p> <p> </p>
Data from: Spatio-temporal changes of Svalbard lagoon systems in the post-Little-Ice-Age period (1936–2021)
Open the record for dataset details and reuse information.
SPACK : spatio-temporal database dedicated to whaling, sealing and fishing in Saint-Paul, Amsterdam, Crozet and Kerguelen Islands between 1780's and 1930's
<p><strong>SPACK is a spatio-temporal database dedicated to whaling, sealing and fishing history</strong>. It aims to gather miscellaneous and scattered sources about whaling, sealing and fishing voyages that visited Saint-Paul, Amsterdam, Crozet and Kerguelen Islands between 1780’s and 1930’s.</p> <p>SPACK has been defined and populated during a PhD thesis in history. The main purpose is to assess the attendance of whaling, sealing and fishing ships around the French Southern Islands from the late 18th century. The goal is also to shed light on the issues arising from the first public policies for managing natural resources once French sovereignty was affirmed in the late 19th.</p> <p>The data collected in SPACK are stored in the object-relational database, PostgreSQL, plus its spatial extension PostGIS. This repository can be used to create a new instance of the SPACK database. It contains 7 SQL files that represent the main tables of the SPACK model.</p> <p>- attested_presence_areas: this table shows the dates on which the vessel is present in the area.</p> <p>- code_areas: this table indicates the codes used to identify each covered area.</p> <p>- code_sealing_gangs: this table shows the code used to indicate when a gang of hunters has been dropped off or relieved on shore by the ship.</p> <p>- natural_resources: this table provides the codes used to classify vessel activity by 'area'.</p> <p>- shipment_origin: this table lists the codes for the main shipowner's geographical origin.</p> <p>- stop_over_voyages: this table describes the date of arrival and departure by 'area'. It also indicates the degree of interpolation of the data, month or day.</p> <p>- voyages_areas: this table contains a list of vessels involved in whaling, fishing and sealing activities that crossed Saint-Paul, Amsterdam, Crozet or/and Kerguelen islands. It provides information such as vessel name, rig type, tonnage, port, shipment origin, natural resource exploited, agent, dates of presence, primary and secondary sources.</p> <p>The main entity of this database is a ship attached to a voyage and a geographical area. This entity is described by a set of properties: ship’s and master’s names, geographical origin, shipowner, port, arrival and departure dates. Those data are featured in the voyages_areas table. The database also provides other helpful information, such as the dates of attendance on the island, the type of natural resource exploited and the sources used to identify a voyage.</p> <p>The SPACK database takes profit from the Whaling History Database (<a href="https://whalinghistory.org/">https://whalinghistory.org/</a>). It does not contain any data imported from WHDB, but it is still possible to link the two sources. Indeed, the voyages_areas table stores the identifier used by the WHDB to describe each voyage.</p> <p>The WHDB provides the vessel's location in lat/lon for several voyages. Those locations have been processed to populate the voyages_areas table and check when a voyage crossed a study area: Saint-Paul, Amsterdam, Crozet or Kerguelen Islands. However, no spatial information is saved in the SQL files. You can contact the authors if you want more information about the spatial analysis techniques used.</p>
Fig. 4 in Spatio-temporal variation of the invasive copepod Oithona davisae in the zooplankton community of Kavala harbour Abstract
Fig. 4: Contribution (%) of Oithona davisae and Oithona nana to the rest of the adult copepods of the zooplankton community collected with the 50 and 200 μm net.
Cross Spatio-Temporal Pathology-based Lung Nodule Dataset
<p>We introduce a novel cross spatio-temporal lung nodule dataset based on pathological information, which effectively integrates rich multimodal information within the spatio-temporal dimension.</p>
Cross Spatio-Temporal Pathology-based Lung Nodule Dataset
<p>We introduce a novel cross spatio-temporal lung nodule dataset based on pathological information, which effectively integrates rich multimodal information within the spatio-temporal dimension.</p>
Figure 6 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 6. Cluster analysis (Bray Curtis Similarity) of the stations sampled (stations 11 and 12 in front of the date, followed by the month number and then supporting the year) during the monitoring of the Araruama lagoon aquatic biota, in function of the zooplankton assemblage richness and abundance.
Development of public dynamic spatio-temporal monitoring and analysis tool of supply chain vulnerability, resilience, and sustainability
<p>Supply chains play a pivotal role in driving economic growth and societal well-being, facilitating the efficient movement of goods from producers to consumers. However, the increasing frequency of disruptions caused by geopolitical events, pandemics, natural disasters, and shifts in commerce poses significant challenges to supply chain resilience. This draft update report discusses the development of a dynamic spatio-temporal monitoring and analysis tool to assess supply chain vulnerability, resilience, and sustainability. Leveraging news data, macroeconomic metrics, inbound cargo data (for sectors in California), and operational conditions of California's highways, the tool employs Natural Language Processing (NLP) and empirical regression analyses to identify emerging trends and extract valuable information about disruptions to inform decision-making. Key features of the tool include sentiment analysis of news articles, topic classification, visualization of geographic locations, and tracking of macroeconomic indicators. By integrating diverse and dynamic data sources (e.g., news articles) and using empirical and analytical techniques, the tool offers a comprehensive framework to enhance our understanding of supply chain vulnerabilities and resilience, ultimately contributing to more effective strategies for decision-making in supply chain management. The dynamic nature of this tool enables continuous monitoring and adaptation to evolving conditions, thereby enhancing the analysis of resilience and sustainability in global supply chains.</p>
Figure 5 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Figure 5 Light microscope and stereomicroscope images of microalgae, macroalgae and invertebrates taken from gut contents of Nacella deaurata.
Figure 4 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Figure 4 Light microscope and stereomicroscope images of microalgae, macroalgae and invertebrates taken from gut contents of Nacella magellanica.
Figure 3 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Figure 3 Non-metric multidimensional scaling of the dietary composition recorded in the gut contents of the Nacella species in Puerto del Hambre (a, c) and Otway Sound (b, d). a, b correspond to the winter months, and c, d to the summer months. The dashed line indicates the separation between species.
Figure 2 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Figure 2 Percentage contribution SIMPER of items in the gut contents of the Nacella species in Puerto del Hambre and Otway Sound for the winter and summer months. The contribution limit was 90% of the total dietary composition. SIMPER analysis shows the dissimilarity between the species of Nacella in the two localities (average dissimilarity in bold and on the bar). The contribution limit was 75% of the total dietary composition. M = Macroalgae (with colours) and I = Invertebrates (with grey scale). Structural hardness of the thallus for macroalgae: th = thin filaments, cf = corticated filaments, clf = cylinder-like form and lm= leathery macrophyte. Functional group for invertebrates: s = sessile and m = mobile.
Figure 1 from: Rosenfeld S, Marambio J, Ojeda J, Rodríguez JP, González-Wevar C, Gerard K, Contador T, Pizarro G, Mansilla A (2018) Trophic ecology of two coexisting Sub-Antarctic limpets of the genus Nacella: Spatio-temporal variation in food availability and diet composition of Nacella magellanica and N. deaurata in the Sub-Antarctic Ecoregion of Magellan . ZooKeys 738: 1-25. https://doi.org/10.3897/zookeys.738.21175
Figure 1 Location of study sites, circle = Puerto del Hambre and square = Otway Sound. Abbreviations: a, d general view of both localities b, e images of the middle intertidal c, f images of the lower intertidal.
Figure 6 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 6 Cases of malaria and density of Anopheles darlingi in the area covered by the Jirau hydroelectric plant, in Rondônia, Brazil in the pre and post-construction phases. Source: SVS / SIVEP - Malaria.
Figure 4 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 4 Hematophagic activity patterns of anophelines in the pre (a) and post-construction (b) phases of the Jirau hydroelectric plant, in Rondônia, Brazil.
Figure 10 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 10. Features of various stages of intense cyanobacteria bloom in the South Caspian in September 2008 in Aqua MODIS true color images of: September 18 (a); September 27 (b).
Figure 5 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 5. Monthly average values of Chl-a concentration in the coastal and open sea areas of the Middle and South Caspian, determined from Aqua MODIS data for 2022.
Figure 1 in Spatio-Temporal Variability of Algal Bloom in the Caspian Sea
Figure 1. Map of the Caspian Sea with isobaths in meters. Blue lines show the boundaries between the North, Middle and South Caspian. Red rectangle marks the area of in situ fieldwork.
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.
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.