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623 results for “Bangladesh”
Figure 7 in New country records, annotated checklist and key to the dacine fruit flies (Diptera: Tephritidae: Dacinae: Dacini) of Bangladesh
Figure 7. Bactrocera (Bactrocera) dorsalis (Hendel). A) Head. B) Head and scutum. C) Abdomen, female. D) Abdomen, male. E–L) Scutum variation in Bangladesh (after Leblanc et al. 2013). M–Q) Abdomen variation in Bangladesh (after Leblanc et al. 2013).
Figure 33 in New country records, annotated checklist and key to the dacine fruit flies (Diptera: Tephritidae: Dacinae: Dacini) of Bangladesh
Figure 33. Zeugodacus (Sinodacus) infestus (Enderlein). A) Head. B) Head and scutum. C) Abdomen, male. D) Wing. E) Lateral view, female. F) Distribution in Bangladesh.
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
<p>Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.</p> <p>Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ("App") for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of "patient-intrinsic" information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.</p> <p>Results:<b> </b>Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 – -0.331) and calibration slope β=1.287 (1.207 – 1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).</p> <p>Conclusion: The app accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the app's potential use in diagnostic and antimicrobial stewardship are underway.</p>
Heel and cord blood datasets for Bangladesh and Zambia cohorts
<div> <div> <div> <div> <p><strong>Background</strong>: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low-income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.</p> <p><strong>Methods</strong>: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.</p> <p><strong>Results</strong>: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).</p> <p><strong>Conclusions</strong>: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.</p> </div> </div> </div> </div>
Data and code for paper "Freihardt (2024): Perceptions of environmental changes among a climate-vulnerable population from Bangladesh. Climatic Change. DOI 10.1007/s10584-024-03678-6"
<p>This dataset contains the temperature, precipitation, erosion, and perception data, as well as the analysis code in R necessary to replicate the results of the paper:</p> <p>Freihardt, J. (2024): Perceptions of environmental changes among a climate-vulnerable population from Bangladesh. Climatic Change, 177, 25. DOI: 10.1007/s10584-024-03678-6.</p>
OLID I: An Open Leaf Image Dataset of Bangladesh's Major Crops
<p>Artificial intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (<em>Solanum lycopersicum</em>), eggplant (<em>Solanum melongena</em>), cucumber (<em>Cucumis sativus</em>), bitter gourd (<em>Momordica charantia</em>), snake gourd (<em>Trichosanthes cucumerina</em>), ridge gourd (<em>Luffa acutangula</em>), ash gourd (<em>Benincasa hispida)</em>, and bottle gourd (<em>Lagenaria siceraria</em>). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.</p>
Spatial Data collection for study spatial transition dynamic of Rohingya settlement in Bangladesh: 1st version
<p>Full open access article can be found in: <a href="https://doi.org/10.1016/j.landusepol.2023.106874">https://doi.org/10.1016/j.landusepol.2023.106874</a></p> <p> </p> <p><strong>Full Changelog</strong>: <a href="https://github.com/ssujit/SpatialTransitionDynamic/commits/version">https://github.com/ssujit/SpatialTransitionDynamic/commits/version</a></p>
"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
Open the record for dataset details and reuse information.
Heel and cord blood datasets for Bangladesh and Zambia cohorts
Open the record for dataset details and reuse information.
Bangladesh - Tropical Cyclone Historical Catalogue
<p><strong>Gridded data for major historical tropical cyclones over Bangladesh.</strong></p> <p>Each tropical cyclone has a 9 member ensemble, and comprises of time series and footprints at resolutions of 4.4km and 1.5km based on the Met Office Unified Model dynamically downscaling ECMWF ERA5 data.</p> <p>There are 12 available variables, including: air temperature, maximum wind gust speed, minimum air pressure at sea level and precipitation amounts, available at a range of temporal scales, including model instantaneous values, and hourly and daily aggregations.</p> <p>The catalogue contains the following tropical cyclones (landfall date): <strong>BOB01</strong> (30/04/1991 00:00), <strong>BOB07</strong> (25/11/1995 09:00), <strong>TC01B</strong> (19/05/1997 15:00), <strong>Akash</strong> (14/05/2007 18:00), <strong>Sidr</strong> (15/11/2007 18:00), <strong>Rashmi</strong> (26/10/2008 21:00), <strong>Aila</strong> (25/05/2009 06:00), <strong>Viyaru</strong> (16/05/2013 09:00), <strong>Roanu</strong> (21/05/2016 12:00), <strong>Mora</strong> (30/05/2017 03:00), <strong>Fani</strong> (04/05/2019 06:00), <strong>Bulbul</strong> (09/11/2019 18:00)..</p> <p><strong>File Types</strong></p> <ul> <li><strong>tsens.*.tar.gz </strong>Time series data for each named storm. Dimensions are typically: forecast_period, forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fpens.*.tar.gz </strong>Time-aggregated data for each ensemble member for each storm. Variables: max gust speed (fg), minimum sea-level pressure (psl), instantaneous u-wind (ua) and v-wind (va) components. Dimensions are typically: forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fp.fg.T1Hmax.tar.gz </strong>A single best estimate gust-speed (fg) footprint with lower, median and upper bounds accounting for ensemble variation, for each names storm. Compressed tar archive containing multiple netCDF files.</li> <li><strong>fp.Rmodels.fg.tar.gz </strong>R GAM model data used to create best estimate netCDF footprints. Compressed tar archive containing output from <em>mgcv</em> gam model saved as an Rdata file.</li> <li><strong>storm_tracks.tar.gz </strong>Storm tracks for each ensemble member of each names storm from Tempest Extremes tracking algorithm. Compressed tar archive containing multiple .DAT text files.</li> </ul>
Risk factors for non-communicable diseases in Bangladesh: Findings of the population-based cross-sectional national survey 2018
<p><span><strong><span>Objectives:</span></strong> To determine the national prevalence of risk factors of non-communicable diseases (NCD) in the adult population of Bangladesh. </span></p> <p><span><strong><span>Design: The study was a </span></strong>population-based national cross-sectional study.</span></p> <p><span><strong><span>Setting:</span></strong> This study used 496 primary sampling units (PSUs) developed by the Bangladesh Bureau of Statistics. The PSUs were equally allocated to each division and urban and rural stratum within each division.</span></p> <p><span><strong><span>Participants:</span></strong> The participants were adults aged 18-69 years, who were usual residents of the households for at least six months, and stayed the night before the survey. Out of 9900 participants, 8185 (82.7%) completed STEP-1 and STEP-2, and 7208 took part in STEP-3. </span></p> <p><span><b>Primary and secondary outcome:</b> The prevalence of behavioral, physical, and biochemical risk factors of NCD. Data were weighted to generate national estimates.</span></p> <p><strong>Results: </strong>Tobacco use was significantly (p<0.05) higher in the rural (45.2%) than the urban (38.8%) population. Inadequate fruit/vegetable intake was significantly (P<0.05) higher in the urban (92.1%) than in the rural (88.9%) population. The mean salt intake per day was higher in the rural (9.0 gm) than urban (8.9 gm) population. Among all, 3.0% had no, 70.9% had 1-2, and 26.2% had ≥3 NCD risk factors. The urban population was more likely to have insufficient physical activity (AOR: 1.2, 95% CI: 1.2–1.2), obesity (AOR: 1.5, 95% CI: 1.5–1.5), hypertension (AOR: 1.3, 95% CI: 1.3–1.3), diabetes (AOR: 1.6, 95% CI: 1.6–1.6), and hyperglycemia (AOR: 1.1, 95% CI: 1.1–1.1).</p> <p><strong>Conclusions: </strong>Considering the high prevalence of the behavioral, physical, and biochemical risk factors, diverse population and high-risk group targeted interventions are essential to combat the rising burden of NCDs. </p>
Data from: Using terrestrial haematophagous leeches to enhance tropical biodiversity monitoring programmes in Bangladesh
1. Measuring mammal biodiversity in tropical rainforests is challenging, and methods which reduce effort while maximizing success are crucial for long-term monitoring programmes. Commonly used methods to assess mammal biodiversity may require substantial sampling effort to be effective. Genetic methods are a new and important sampling tool on the horizon, but obtaining sufficient DNA samples can be a challenge. 2. We evaluated the efficacy of using parasitic leeches Haemadipsa spp., as compared to camera trapping, to sample biodiversity. We collected 200 leeches from four forest patches in northeast Bangladesh, and identified recent vertebrate hosts using Sanger sequencing of the 16S rRNA gene extracted from each individual leech's blood meals. We then compared this data to species data from camera trapping conducted in the same forest patches. 3. Overall, 41.9% of sequenced leeches contained amplifiable non-human mammal DNA. Four days of collecting leeches led to the identification of 12 species, compared to 26 species identified in 1334 camera trap nights. 4. Synthesis and applications. After assessing the cost, effort, and power of each technique, there are pros and cons to both camera trapping and leech blood meal analysis. Camera trapping and leech collection appear to be complementary approaches. When used together, they may provide a more complete monitoring tool for mammal biodiversity in tropical rainforests. Managers should consider adding leech collection to their biodiversity monitoring toolkit, as improved information will allow managers to create more effective conservation programmes. R scripts are available upon request.
bangladesh-ICT-overview-data v1.0
<p>This is the review release of data underlying the findings presented in an article under review. Descriptions to be updated upon article publication</p>
Replication Package for: "Adapting to Climate Risk with Guaranteed Credit: Evidence from Bangladesh"
<p>Contains the code and publicly available datasets to replicate "Adapting to Climate Risk with Guaranteed Credit: Evidence from Bangladesh". Simulated data sets are provided in place of confidential datasets. See Readme file for details on how to obtain confidential data. </p>
BDFoodSent: A Large-Scale Sentiment-Labeled Restaurant Review Dataset from Bangladesh
<p>BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.</p> <p> </p> <h3>Dataset Statistics</h3> <p>Total Reviews: 334,119</p> <p>Features/Columns: 19</p> <p> </p> <h3>Potential Applications</h3> <p>Sentiment Analysis</p> <p>Restaurant Review Classification</p> <p>Customer Satisfaction Analysis</p> <p>Opinion Mining</p> <p>Natural Language Processing Research</p> <p>Food Service Industry Analysis</p>
Impact of Community Masking on COVID-19: A Cluster-Randomized Trial in Bangladesh
<p>We ran a randomized trial of mask promotion in Bangladesh; the intervention increased mask-use and reduced symptomatic SARS-CoV-2 infections.</p>
Paharpur (Bangladesh). Charter of Budhagupta dated year 159 (side 1).
<p><a href="https://siddham.network/inscription/in00065/">IN00065</a> Paharpur (Bangladesh). Charter of Budhagupta dated year 159 (side 1).</p> <p> </p> <p> </p>
Paharpur (Bangladesh). Charter of Budhagupta dated year 159 (side 2).
<p><a href="https://siddham.network/inscription/in00065/">IN00065 </a>Paharpur (Bangladesh). Charter of Budhagupta dated year 159 (side 2).</p>
Availability and use of technology for e-learning in Bangladesh
<p>There are 1162 responses in the dataset. Respondents’ gender, division, and residence were obtained. Here, 6 items stand (“availability_item_1” to “availability_item_6”) for availability and 11 items (“usability_item_1” to “usability_item_11”) for use of technology measure, where the initial responses were in 5 points Likert scale (1 for “strongly disagree” and 5 for “strongly agree”). On the other hand, 14 items’ (“pss_item_1” to “pss_item_14”) PSS scale was used for measuring stress, where responses were also in five-point Likert scale (0 for “Never” and 4 for “Very often”). However, reverse scoring for items 4, 5, 7, & 8 are given in this data set, according to the instruction of scale scoring. In the dataset, “availability_item_1_binary” to “availability_item_6_binary” and “usability_item_1_binary” to “usability_item_11_binary” represent the categories (1 for “sub-optimum” and 2 for “optimum”) of the availability and usability of technology measure.</p>
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.