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.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
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. 1. Карта иЗученного побереЖьЯ Северной Кореи (пров. Хамгён-пукто) с укаЗанием населенных пунктов Чипсам и ЁмбудЖин и видами берегов: а, b, c – побереЖье вблиЗи Чипсам; d, e, f – побереЖье вблиЗи ЁмбудЖин. Fig. 2. A map of the area studied along the coast of North Korea (North Hamgyong Province) with indication of two sites, Jipsam and Yombunjin, and coastline views: а, b, c – coasts of Jipsam; d, e, f – coasts of Yombunjin. in On the bivalve molluscan fauna of North Hamgyong Province (North Korea)
Рис. 1. Карта иЗученного побереЖьЯ Северной Кореи (пров. Хамгён-пукто) с укаЗанием населенных пунктов Чипсам и ЁмбудЖин и видами берегов: а, b, c – побереЖье вблиЗи Чипсам; d, e, f – побереЖье вблиЗи ЁмбудЖин. Fig. 2. A map of the area studied along the coast of North Korea (North Hamgyong Province) with indication of two sites, Jipsam and Yombunjin, and coastline views: а, b, c – coasts of Jipsam; d, e, f – coasts of Yombunjin.
Рис. 1. Места отбора проб телеуправлЯемым подводным аппаратом в Охотском море. Fig. 1. The map of the studied area in the Sea of Okhotsk. in Rare and interesting deep-sea finds of the buccinid gastropods (Gastropoda: Buccinidae) from the Sea of Okhotsk
Рис. 1. Места отбора проб телеуправлЯемым подводным аппаратом в Охотском море. Fig. 1. The map of the studied area in the Sea of Okhotsk.
Data and code to map the genomic potential for photosynthesis in piconanoplankton
<p>This archive contains the R and Python scripts, model outputs and technical documentation corresponding to an application of the Bluecloud Plankton Genomic Virtual Lab workflow to map the genomic potential for photosynthesis in piconanoplankton.</p> <p>Alternatively, the workflow is also accessible via the bluecloud catalogue at <a href="https://data.d4science.net/Zraq">https://data.d4science.net/Zraq</a> or at the following Github repository <a href="https://github.com/alexschickele/bluecloud/releases/tag/v1.0">https://github.com/alexschickele/bluecloud/releases/tag/v1.0</a></p> <p> </p>
Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue
<p>Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following<em> in vivo</em> exposure to house dust mite extract.</p> <p>Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<span><a href="https://github.com/targeted-lipidomics/quantMSImageR"><span>https://github.com/targeted-lipidomics/quantMSImageR</span></a></span><span>).</span></p>
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>
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
<p>A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French <em>Arabidopsis thaliana</em> population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.</p>
Data and code for high-resolution climate-resilient corridor mapping in southwestern Costa Rica (beta)
<p>High-resolution mapping and validation of potential climate-resilient corridors in southwestern Costa Rica. A fully documented, complete version of this repository will be archived with a DOI upon manuscript publication.</p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)
<h1><strong>Overview</strong></h1> <p>The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023. </p> <h1><strong>Maps Accuracy</strong></h1> <p>The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1.</p> <p>Table 1: Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class.</p> <table> <tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2019</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2020</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2021</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2022</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2023</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> </tr> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> </tr> <tr> <td> <p><span>Forest</span></p> </td> <td> <p><span>0.87</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>1</span></p> </td> </tr> <tr> <td> <p><span>Non Forest</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>1</span></p> </td> <td> <p><span>0.87</span></p> </td> </tr> <tr> <td> <p><strong><span>Overall Accuracy</span></strong></p> </td> <td> <p><strong><span>0.92</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.90</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.94</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> </tr> </tbody> </table> <h1><strong>Files descripion</strong></h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul> <p> </p>
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions). in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 1. Карта-схема литоральных Экспедиций Института биологии морЯ (Национального научного центра морской биологии им. А.В. Жирмунского ДВО РАН) и Дальневосточного государственого университета (Дальневосточного федерального университета) (по: Ivanova, Tsurpalo [2012], с дополнениЯми). Fig. 1. A schematic map of intertidal expeditions performed by the Institute of Marine Biology (A.V. Zhirmunsky National Scientific Centre of Marine Biology FEB RAS) and Far Eastern State University (Far Eastern Federal University) (after Ivanova, Tsurpalo [2012], with additions).
A framework for spatial map generation using acoustic echoes for robotic platforms
<div> <div> <div> <div> <div> <div> <p>In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%.</p> </div> </div> </div> </div> </div> </div>
Рис. 1. Схема расположениЯ поселениЯ Константиновка-1 на карте ПриморьЯ. Fig. 1. A location map of the Konstantinovka-1 archaeological site in Primorye. in Mollusks from the archaeological site Konstantinovka-1 in Primorye (Russian Far East)
Рис. 1. Схема расположениЯ поселениЯ Константиновка-1 на карте ПриморьЯ. Fig. 1. A location map of the Konstantinovka-1 archaeological site in Primorye.
The global 30-m mangrove canopy height map for 2020
<p><span>The global mangrove canopy height map with a resolution of 30 m for 2020 (GlobeMCH_2020_30m_v1) was generated by integrating the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeMCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the mangrove canopy height value is meter. The GlobeMCH_2020_30m_v1 was divided into 101 files, and the range of each file is 11°×11° (10° + 1° buffer).</span></p>
OrphaCode as a non-standard with mappings to a standard vocabulary in OMOP
<p>The files contain the necessary tables for the introduction of OrphaCode as a non-standard with mappings to a standard vocabulary in OMOP. These include:</p> <ul> <li>concept_manual</li> <li>concept_relationship_manual</li> <li>concept_synonym_manual.</li> </ul> <p>This file has not yet been quality assured, but will be made available for validation.</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
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles) in The Taxonomic Composition And Quantitative Indicators Of Zoobenthos In The Downstream Of The Bolshaya Pyora River (Zeya River Basin, Amur Region)
Рис. 1. Карто-схема реки БоΛьшая Пёра с указанием мест отбора проб (обозначены кружками) Fig. 1. Maps of the Bolshaya Pyora River with sampling locations (marked by circles)
Georeferenced and cropped "63k Maps of Burma"
<p>Georeferenced (to WGS1984) and cropped set of about 820 historic maps of Burma at a scale of 1 inch per mile (63,360) covering about 75% of the country. Those topographic maps, originally produced and published by the Great Trigonometrical Survey of India between 1899 and 1946, have been scanned and shared with the public as part of the "Old Survey Of India Maps” Community under a CC BY 4.0 International Licence. Many of these maps are reprints of earlier maps produced before the war. Most mapsheets are early editions (edition 1 or edition 2).</p> <p>Each of the 820 map sheet scans was georeferenced using the Latitude-Longitude corner coordinates in Everest 1830 projection. Those map sheets were cropped, keeping only the map area - to allow a seamless mosaic without the mapframe overlapping adjacent map sheets when several map sheets are put together in a GIS. Those cropped map sheets were projected from Everest 1830 to WGS1984 (EPSG4326) - standard GPS - projection to make them easier to use and combine with other GIS data.</p> <p>Those map sheets can be loaded directly in any GIS such as QGIS or ESRI ArcGIS as well as Google Earth.</p> <ul> <li>The mm_OI_JBv2024 folder contains the cropped end georeferenced map sheets in jpg-format as well as accompagning georeference and metadata incl.<br> <ul> <li>The mm_OI_JBv2024_kmlLinks contains kml files to easily load the mapsheets into Google Earth</li> <li>The mm_historicOI_EPSG4326.gdb contains an ESRI mosaic dataset to easily load all mapsheets into ArcGIS</li> </ul> </li> <li>The mm_OI_JBv2024_scanMaps folder contains the uncropped original map scans (renamed though) in jpg-format.</li> <li>The mm_topoOI_JBv7_masterlist.xlsx is a masterlist cataloguing all map sheets for easier use and matching them with the original source files as shared as part of the "Old Survey Of India Maps" (e.g. to identify new mapsheets should new maps be released)</li> <li>The indexMaps folder contains small scale index maps to locate the map sheets using their map sheet Grid-Letter-nomenclature</li> </ul> <p>All georeferenced map scans are based on maps shared by John Brown via Zenodo</p> <ul> <li><a href="../records/8040798">https://zenodo.org/records/8040798</a> (63k Maps of Burma, version 7, Published June 14, 2023)</li> <li><a href="../records/10463372">https://zenodo.org/records/10463372</a> (63k Maps of Burma--additional 1--20240105, version 1, Published January 5, 2024)</li> </ul> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a <strong><em>number</em></strong> from 1 to 16 to indicate the number of the map in the 1 degree block. </p> <p>This <strong><em>Number Letter Number</em></strong> designation is followed by the <strong>map series type</strong> either OI (contains a LCC grid) or OILatLon (only has a Lat-Lon grid), followed by the <strong>edition and year of the edition</strong>, followed by the <strong><em>date of publication/print</em></strong>. If the information is not available an "X" (for edition) or "0000" (for an unknown year) is used. A best-guess approach was used if the edition and print year and version information was ambiguous.</p> <p>The files as shared via the "<a href="https://zenodo.org/records/11661876">Old Survey Of India Maps</a>" have been renamed to standardize the file naming, sometimes correcting them and to make them unique in the case several editions of the same map sheet were available. </p> <p>A topographical index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
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.