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623 results for “Bangladesh”
Replication material for paper "Freihardt (2025): Trapped by climate change? (In)voluntary immobility in Bangladesh. Regional Environmental Change. DOI 10.1007/s10113-025-02452-3."
<p>This is the data and replication code underlying the paper:</p> <p>Freihardt, J. Trapped by climate change? (In)voluntary immobility in Bangladesh. <em>Reg Environ Change</em> <strong>25</strong>, 117 (2025). https://doi.org/10.1007/s10113-025-02452-3</p>
Primary data on the Condition of the Stray dogs in Sherpur sadar Upazilla, Bangladesh
<p>This dataset contains primary data collected from Sherpur Sadar Upazilla, Bangladesh, focusing on the welfare and health conditions of stray dogs. The data was gathered through field observations, surveys, and interviews with local residents and animal welfare organizations. It includes detailed information on the physical condition, health indicators (such as signs of malnutrition, injury, and disease), behaviors, and environmental conditions of stray dogs in the region. This dataset aims to provide a comprehensive understanding of the challenges faced by stray dogs in Sherpur Sadar Upazilla, with a focus on issues related to nutrition, healthcare, and overall well-being. The data is intended to support research on animal welfare and inform policies for improving the living conditions of stray animals in Bangladesh.</p>
High-resolution inundation dataset for coastal India and Bangladesh
<p>This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh.</p> <p><strong>Input data:</strong></p> <p>These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH.</p> <p><strong>Methods:</strong></p> <p>We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water.</p> <p>We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions.</p> <p><strong>Data set format:</strong></p> <p>The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Data set for download:</strong></p> <p>A. Three data layers for Odisha, India:</p> <ol> <li>OD_pre_binary.tif</li> <li>OD_post_binary.tif</li> <li>OD_inundation.tif</li> </ol> <p>These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.</p> <p>B. Three data layers for West Bengal, India:</p> <ol> <li>WB_pre_binary.tif</li> <li>WB_post_binary.tif</li> <li>WB_inundation.tif</li> </ol> <p>These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.</p> <p>C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region.</p> <ol> <li>BNG_L_pre_binary.tif</li> <li>BNG_L_post_binary.tif</li> <li>BNG_L_inundation.tif</li> <li>BNG_U_pre_binary.tif</li> <li>BNG_U_post_binary.tif</li> <li>BNG_U_inundation.tif</li> </ol> <p>The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.</p> <p>The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.</p>
National Checklists 2017: Bangladesh 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 Bangladesh collected using effechecka and geonames polygons
National Checklists 2019: Bangladesh 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 Bangladesh collected using effechecka and geonames polygons
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Dataset for paper "Freihardt (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment. DOI 10.1007/s11111-025-00478-7"
<p>This is the dataset underlying the paper: </p> <p>Freihardt, J. (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment, 47, 6. DOI: 10.1007/s11111-025-00478-7.</p>
Figure 1 in First record of Marbled snake eel, Ophichthus lithinus (Jordan & Richardson 1908) (Ophichthidae: Anguilliformes) from Bangladesh marine water
Figure 1. Sampling location of Ophichthus lithinus collected from St. Martin's Island of Bangladesh () and nearest distribution of Ophichthus lithinus (★).
Figure 2.a in First record of Marbled snake eel, Ophichthus lithinus (Jordan & Richardson 1908) (Ophichthidae: Anguilliformes) from Bangladesh marine water
Figure 2.a. Jaw teeth of Ophichthus lithinus, b. Head of O. lithinus, and c. Lateral view of O. lithinus (SL. 693mm).
Figure 3 in First record of Marbled snake eel, Ophichthus lithinus (Jordan & Richardson 1908) (Ophichthidae: Anguilliformes) from Bangladesh marine water
Figure 3. Dentition on upper jaw and lower jaw of snake eel, Ophichthus lithinus, from Saint Martin's Island, Bangladesh Bay of Bengal, India (SL. 693mm).
Figure 2 in First record of Lestranicus transpectus (Moore, 1879) and Graphium macareus (Godart, 1819) (Insecta: Lepidoptera: Papilionoidea) in Bangladesh
Figure 2. Lestranicus transpectus basking on the leaf (underside view). / Lestranicus transpectus tomando el sol sobre una hoja (vista inferior).
Figure 1 in First record of Lestranicus transpectus (Moore, 1879) and Graphium macareus (Godart, 1819) (Insecta: Lepidoptera: Papilionoidea) in Bangladesh
Figure 1. New locality record of Lestranicus transpectus and Graphium macareus in Bangladesh. / Nuevos registros de localidad de Lestranicus transpectus y Graphium macareus en Bangladesh.
Figure 3 in First record of Lestranicus transpectus (Moore, 1879) and Graphium macareus (Godart, 1819) (Insecta: Lepidoptera: Papilionoidea) in Bangladesh
Figure 3. Graphium macareus puddling on the stone (underside view). / Graphium macareus alimentandose sobre una piedra (vista inferior).
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
National Checklists: Bangladesh 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>
Intention to utilize telehealth service in Bangladesh
<p>This data consists of information on participants' knowledge, perceived benefit, perceived concerns, and predispositions related to telehealth services in Bangladesh. In the data set, k1 to k5 indicted the items of knowledge. Similarly, pb1 to pb4 indicate perceived benefit, pc indicates the item of perceived concern, and pd1 to pd2 indicates the items of predisposition. This data set also includes information related to the demographic and perceived health status information. </p>
Figure 4 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 4: Isolate UE-5 (Aspergillus terreus). (A) Surface of colony, on potato dextrose agar after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia, conidiophores and conidia after 5 days culture. (D) Conidiophore with conidia. (E) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 3 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 3: Isolates UE-3 (Curvularia sp., A–C) and UE-4 (Curvularia moringae, D–H). (A) Surface of colony, on potato dextrose agar (PDA) after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia and conidia after 7 days culture. (D) Surface of colony, on PDA after 12 days culture at 28 °C. (E) Reverse of colony. (F) Mycelia and conidia after 7 days culture. (G) Conidium. (H) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
Figure 6 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 6: Antimicrobial activity of the crude extracts obtained from marine endophytic fungi associated with Ulva sp. against five bacteria (Staphylococcus aureus, Bacillus megaterium, Escherichia coli, Salmonella typhi, Pseudomonas aeruginosa) and one fungus (Aspergillus flavus). Values are mean ± standard deviation, n = 3. Bars with different letters are significantly different according to Tukey's post hoc test at p = 0.05. Note: The solvent control (dichloromethane) showed no inhibition (0 mm). The strongest inhibitory effects were observed with the positive controls kanamycin (S1) and ketoconazole (S2).
Figure 2 in Bioactivity and chemical screening of endophytic fungi associated with the seaweed Ulva sp. of the Bay of Bengal, Bangladesh
Figure 2: Isolate UE-2 (Nigrospora magnoliae). (A) Surface of colony, on potato dextrose agar after 6 days culture at 28 °C. (B) Reverse of colony. (C) Mycelia and conidia after 21 days culture. (D) Conidiogenus cells with conidia. (E) Phylogenetic tree inferred from internal transcribed spacer sequences using maximum likelihood method.
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)
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DANDI Archive for NWB datasets
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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.