Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
819
datasets available to search
ShareScore release 0.9.0
Dataset results
819 results for “Paraguay”
Dataset: Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay
<p><i><strong>"Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el cálculo del Índice de Pobreza Energética Multidimensional (MEPI) para el caso de estudio. </p><ol><li>MEPI_CarmenSoler_Data_2018_CHILECON2023.xlsx</li></ol><p>En el archivo, podrán encontrar los extraídos de los resultados de la encuesta realizada en el 2018 por un equipo de investigadores paraguayos (En el artículo podrán encontrar más información). Además de los datos, podrán ver todos los pasos y cálculos llevados a cabo para obtener los resultados obtenidos. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
National Checklists 2017: Paraguay Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Paraguay collected using effechecka and geonames polygons
National Checklists 2019: Paraguay Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Paraguay collected using effechecka and geonames polygons
Fig. 2 in Comparative cytogenetics of Astyanax (Teleostei: Characidae) from the upper Paraguay basin
Fig. 2. Karyotypes of Astyanax abramis (A and B), A. lacustris (C and D) and A. pirapuan (E and F) after Giemsa-stained and showing the distribution of constitutive heterochromatin revealed by C-banding. Insets show Ag-NOR and 18S-bearing chromosomes pairs. Scale bar = 10 µm.
Fig. 3 in Comparative cytogenetics of Astyanax (Teleostei: Characidae) from the upper Paraguay basin
Fig. 3. Metaphase of Astyanax pirapuan after fluorescent in situ hybridization with 18S probe. Arrows indicate the marked chromosomes. Scale bar = 10 µm.
Fig. 5 in Description of a new species of Moenkhausia (Characiformes: Characidae) from the upper Paraguay basin, Central Brazil, with comments on its phylogenetic relationships
Fig. 5. Map showing the localities of Moenkhausia flava. Red star represents the type locality. Black square represents the Salto das Nuvens fall and the white square represents Salto Maciel fall.
Fig. 4 in A new species of bumblebee catfish of the genus Microglanis (Siluriformes: Pseudopimelodidae) from the upper rio Paraguay basin, Brazil
Fig. 4. Scatter diagram of Sheared Principal Components analysis of combined samples of Microglanis leniceae (diamond, n = 6), M. carlae (squares, n = 9), and M. cottoides (triangle, n = 7).
Fig. 1 in A new species of bumblebee catfish of the genus Microglanis (Siluriformes: Pseudopimelodidae) from the upper rio Paraguay basin, Brazil
Fig. 1. Microglanis leniceae, holotype, ZUFMS 4148, 33.0 mm SL, rio Betione, Miranda, Mato Grosso do Sul State, Brazil.
Fig. 3 in A new species of bumblebee catfish of the genus Microglanis (Siluriformes: Pseudopimelodidae) from the upper rio Paraguay basin, Brazil
Fig. 3. Geographic distribution of Microglanis leniceae in states of Mato Grosso (MT) and Mato Grosso do Sul (MS) (yellow star = type locality). Brazilian states acronyms: GO = Goiás; SP = São Paulo.
Fig. 2 in A new species of bumblebee catfish of the genus Microglanis (Siluriformes: Pseudopimelodidae) from the upper rio Paraguay basin, Brazil
Fig. 2. Dorsal view of right pectoral-fin spine of holotype (ZUFMS 4148, 33.0 mm SL) of Microglanis leniceae, from the upper rio Paraguay basin, Mato Grosso State, Brazil. Scale bar = 1 mm.
Figure 1-4. Habitus photographs. 1 in Preliminary report on the Myrmeleontidae (Neuroptera) of Paraguay
Figure 1-4. Habitus photographs. 1) Dimares elegans (Perty) [holotype female of Myrmeleon conicollis Walker]. 2) Dimarella riparia Navás lectotype male. 3) Dimarella praedator (Walker) holotype female. 4) Eremoleon pulchra (Esben-Petersen) holotype female. 5) Glenurus peculiaris (Walker) [holotype of Glenurus brasiliensis Navás]. 6) Ameromyia hirsuta Navás [holotype female of Ameromyia longiventris Navás]. 7) Argentoleon longitudinalis (Navás) holotype female. 8) Austroleon immitus (Walker) holotype female.
Infrastructure Climate Resilience Assessment Data Starter Kit for Paraguay
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Fig. 4 in Anteaters on the edge: giant and lesser anteaters (Myrmecophaga tridactyla and Tamandua tetradactyla) at their geographic distributional limits in Paraguay
Fig. 4. Capture success (%) of lesser anteater (Tamandua tetradactyla Linnaeus, 1758) and giant anteater (Myrmecophaga tridactyla Linnaeus, 1758) in the Humid Chaco Ecoregion in Paraguay using trap-cameras from November 2016 to March 2018 by forest types: W-RF, Riparian forests associated to wetlands; MXF, Mesoxerophytic semi-deciduous forests dominated by Schinopsis balansae; FSF, Floodable sub-humid forest islets.
Figs 2, 3 in Anteaters on the edge: giant and lesser anteaters (Myrmecophaga tridactyla and Tamandua tetradactyla) at their geographic distributional limits in Paraguay
Figs 2, 3. Photos taken by trap cameras in the Humid Chaco Ecoregion in Paraguay: 2) Giant anteater (Myrmecophaga tridactyla Linnaeus, 1758) on March 20th, 2017; 3) Lesser anteater (Tamandua tetradactyla Linnaeus, 1758) on November 28th, 2017.
Fig. 1 in Anteaters on the edge: giant and lesser anteaters (Myrmecophaga tridactyla and Tamandua tetradactyla) at their geographic distributional limits in Paraguay
Fig. 1. Location of the study area in the Humid Chaco Ecoregion in Paraguay (left) and camera-trap ubications from November 2016 to March 2018. The numbers of the amplified area (right) indicate the date and season in which the trap cameras were placed in the different forests types (see Tab. I).
Fig. 5 in Anteaters on the edge: giant and lesser anteaters (Myrmecophaga tridactyla and Tamandua tetradactyla) at their geographic distributional limits in Paraguay
Fig. 5. Records (%) by hour of the day of giant anteater (Myrmecophaga tridactyla Linnaeus, 1758) and lesser anteater (Tamandua tetradactyla Linnaeus, 1758) in the Humid Chaco Ecoregion in Paraguay using trap-cameras from November 2016 to March 2018.
National Checklists: Paraguay Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 1 in A new species of Microstachys (Euphorbiaceae, Hippomaneae) in Paraguay
Fig. 1. – Microstachys dasycarpa Pscheidt, Esser & Cordeiro. A. Habit; B. Leaf; C. Leaf indumentum; D. Glands; E. Inflorescence; F. Female flower in frontal view with bract; G. Female flower in lateral view with bract; H. Female flower; I. Male flower in lateral view. [Schinini & Palacios 25685, CTES]
Fig. 4. – Mimosa sensitiva L. var. sensitiva. A in Nuevas citas del género Mimosa (Mimosoideae, Leguminosae) para la flora del Paraguay
Fig. 4. – Mimosa sensitiva L. var. sensitiva. A. Rama florífera; B. Detalle de margen de folíolo; C. Flor. [A-B: Zardini 54059, BB; C: Zardini 54636, BB]
Fig. 5. – Mimosa serra Burkart. A in Nuevas citas del género Mimosa (Mimosoideae, Leguminosae) para la flora del Paraguay
Fig. 5. – Mimosa serra Burkart. A. Racimo; B. Sección del tallo y pecíolo; C. Folíolo, cara adaxial; D. Folíolo, cara abaxial; E. Flor; F. Bráctea floral; G. Flor. [Zardini 44725, BB]
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.