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997 results for “awareness”
Cardiovascular Disease Risk of Awareness
ClinicalTrials.gov study NCT06000436. IPD Sharing: NO. Countries: 0. Publications: 0.
AI4MARS: A Dataset for Terrain-Aware Autonomous Driving on Mars
This dataset was built for training and validating terrain classification models for Mars, which may be useful in future autonomous rover efforts. It consists of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by 10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers.
An allele-aware approach identifies altered nuclear associations of the contracted D4Z4 array at the FSHD locus
GEO Series GSE40673. Homo sapiens. 10 samples. Type: Other.
Diversity-aware Fairness Testing of Machine Learning Models through Hashing-based Sampling
<h4>VBT-X</h4><p>The results of vbt-x.</p><p>```</p><p>asdasd</p><p>```</p>
Evaluation Data for "Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation"
<p><strong>Contents</strong><br> <br> The four CSV files are the data used for the evaluation in:</p> <ul> <li>Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.</li> <li>DOI: <a href="http://doi.org/10.1007/978-3-030-45439-5_15">10.1007/978-3-030-45439-5_15</a></li> <li>Code: <a href="https://github.com/IllDepence/ecir2020">github.com/IllDepence/ecir2020</a></li> </ul> <p>The evaluation was conducted in a citation re-prediction setting.</p> <p><strong>CSV Format</strong></p> <ul> <li>7 columns divided by \u241E <ol> <li>cited document ID <ul> <li>for *_nomarker.csv: citation marker position ambiguous</li> <li> for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context</li> </ul> </li> <li>adjacent cited document IDs <ul> <li>only given in citrec_unarxive_*.csv</li> <li>divided by \u241F</li> <li>order matches 'CIT' markers in citation context</li> </ul> </li> <li>citing document ID</li> <li>citation context</li> <li>MAG field of study IDs <ul> <li>divided by \u241F</li> </ul> </li> <li>predicate:argument tuples generated based on PredPatt <ul> <li>JSON</li> </ul> </li> <li>noun phrases <ul> <li>for *_nomarker.csv: divided by \u241F</li> <li>for *_withmarker.csv: <ul> <li>divided by \u241D into</li> <li>noun phrases</li> <li>noun phrase directly preceding citation marker</li> </ul> </li> </ul> </li> </ol> </li> </ul> <p><strong>Data Sources</strong></p> <ol> <li>citrec_unarxive_cs_withmarker.csv <ul> <li>data set <ul> <li>unarXive</li> <li>Paper DOI: <a href="http://doi.org/10.1007/s11192-020-03382-z">10.1007/s11192-020-03382-z</a></li> <li>Data DOI: <a href="http://doi.org/10.5281/zenodo.2553522">10.5281/zenodo.2553522</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science</li> <li>cited doc is cited at least 5 times</li> </ul> </li> </ul> </li> <li>citrec_mag_cs_en.csv <ul> <li>data set <ul> <li>Microsoft Academic Graph (MAG)</li> <li>Paper DOI: <a href="http://doi.org/10.1145/2740908.2742839">10.1145/2740908.2742839</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science and in English</li> <li>citing doc abstract in MAG given</li> <li>cited doc is cited at least 50 times</li> </ul> </li> </ul> </li> <li>citrec_refseer.csv <ul> <li>data set <ul> <li>RefSeer</li> <li>Paper URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">ojs.aaai.org/index.php/AAAI/article/view/9528</a></li> <li>Data URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">psu.app.box.com/v/refseer</a></li> </ul> </li> <li>filter <ul> <li>for citing and cited docs title, venue, venuetype, abstract, and year not NULL</li> </ul> </li> </ul> </li> <li>citrec_acl-arc_withmarker.csv <ul> <li>data set <ul> <li>ACL ARC</li> <li>Paper URL: <a href="http://aclanthology.org/L08-1005">aclanthology.org/L08-1005</a></li> <li>Data URL: <a href="http://acl-arc.comp.nus.edu.sg/">acl-arc.comp.nus.edu.sg/</a></li> </ul> </li> <li>filter <ul> <li>cited doc has a DBLP ID</li> </ul> </li> </ul> </li> </ol> <p> </p> <p><strong>Paper Citation</strong></p> <pre>@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, } </pre>
Data set of Sepsis awareness at the university hospital level: a survey-based cross-sectional study
<p><strong>This is the dataset for the SAfE survey</strong></p> <p>We conducted a survey on nurses and physicians distributed over all adult departments of the Lausanne University Hospital (LUH) and local paramedics. The survey aimed to assess professionals’ demographics (age, profession, seniority, unit of activity), prior sepsis education, perceptions, knowledge of sepsis epidemiology, definition, recognition and management. Correlation between surveyed personel and sepsis perceptions and knowledge were assessed with univariate logistic regression models.</p> <p><strong>Methods:</strong></p> <p>The research team designed a survey inspired from previously published surveys assessing knowledge and awareness of sepsis.</p> <p>The questions were tailored to the profession (clinical scenario adapted activity sector - medicine, surgery, emergency department or gynecology). The survey was written and completed in French. Each section of the survey (paramedics’, nurses’ and physicians’ section) was submitted to three focus groups consisting of 3 to 6 participants of each profession, commonly involved in care of patients with sepsis. These focus groups assessed the applicability and appropriateness (validity) of the survey. The focus group were constituted of nurses, physicians and paramedics of all seniority levels. Their primary task was in assessing whether formulations and relevance of questions were adequate. The survey was revised using feedback from the groups. Surveys of nursing staff and paramedics were more focused on screening, initial evaluation and early management whereas physicians were also tested on diagnosis and management. Responses options included Likert-type scales, binary (e.g. “yes/no”) or multiple choices. Each question was locked upon answering, which prevented post hoc changes that could be influenced by information provided at a later stage of the survey. The final survey contained questions on participants’ demographic characteristics (5/7/6 questions for nurses/paramedics/physicians), sepsis continuous education (3/3/3 questions), self-evaluation of sepsis knowledge and clinical management (2/2/2 questions), definitions, scores and epidemiology (11/12/14 questions), and sepsis management (4/4/5 questions). The survey was developed in REDCap (Research Electronic Data Capture) software so as to automatically export participants’ responses to a database. Surveys are provided as supplementary material (supp. meth. survey).</p> <p>Participants were recruited between January 20 and October 10, 2020. We aimed for a convenience sample size of 1,000 persons (approx. 20% of the active HCPs) distributed over all departments (Emergency department (ED), intensive care unit (ICU), Medicine, Paramedic, Psychiatry, or Surgery) and professions (paramedics, nurses and physicians) to reach 20% of LUH staff considered HCPs, being as representative as possible. Pediatrics and neonatology staff (not covered by Sepsis-3 consensus definitions) as well as nurses and physicians not in daily contact with patients (i.e., who were working in research team or in administration) were excluded. We favored a supervised approach rather than a dissemination of the survey to all HCPs by email. Participants answered the online survey under trained interviewer supervision so as maximize data quality and to avoid biased responses (internet queries, discussions between colleagues). Furthermore, to avoid multiple answers by a same HCP, surveys were accessed by QR-code only available at screening; timing of survey completion was registered and email addresses were registered.</p> <p>Thus, participants were screened amongst the medical (n=1664) and nursing staff (n=2463) in daily contact with patients of LUH and amongst paramedics of the Canton of Vaud (n=290) during the screening period. Screening by trained interviewers took place during scheduled patient hand-offs, seminars or group meetings, as permitted by heads of units. Participation was voluntary and anonymous. Participants completed the online survey using tablets or smartphones (participants’ or provided by the investigators).</p> <p><strong>Results:</strong></p> <p>Between January and October 2020, 1,116 of 1,216 contacted professionals completed the survey (participation rate 91.8%). These participants represented about 25% of the workforce (n=4417) – i.e., 25.1% of nurses (619/2,463), 20.9 % of physicians (348/1,664),and 51.4% of paramedics (149/290). Only 13% of participants (physicians: 28.4%, nurses: 5.9%, paramedics: 6.8%) correctly identified the Sepsis-3 consensus definition. Similarly, less than 50% of participants (physicians: 48.6%, nurses: 10.0%) selected the SOFA score as a sepsis defining score for infected patients. Furthermore, 24% of participants properly identified the qSOFA score as a predictor of increased mortality; and 6% selected correctly its components. For a suspected sepsis, 96.1%, 91.6% and 75.8% of physicians respectively chose blood cultures, broad-spectrum antibiotics and fluid resuscitation as required interventions; 76.4% and 18.2% of physicians requested initial measures within 1 and 3 hours, respectively. For physicians, recent training correlated with awareness regarding definitions, SOFA and qSOFA score use and components: ORs (95%CI) 2.2 (1.4-3.6), 4.3 (2.7-6.7), 3.4 (2.2-5.2), and 2.6 (1.5-4.6), respectively).</p> <p><strong>Conclusions:</strong></p> <p>We identify a deficit of awareness among physicians, nurses and paramedics at LUH correlating with a lack of sepsis-specific training.</p>
Awareness of Big Data issues
<p>Data from the survey of Big Data awareness done in 2018-19 at the University of Economics in Prague.</p>
Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"
<p>This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"</p> <p>Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</p>
Re-aware Global Ocean Deoxygenation During the Past 140 Years
<p>The intensifying global ocean deoxygenation has significant impacts on the oceanic ecological equilibrium. Gaps persist in understanding the dynamic evolution of global ocean oxygen throughout the 20th century due to the spatiotemporal sparsity of measurements and systematic biases within Earth system models. Here, we propose a novel spatiotemporal embedded artificial intelligence model that integrates diverse ocean datasets to construct a high-precision, monthly-resolved global ocean oxygen dataset spanning the past 140 years. Our quantitative analyses revealed a decline in global ocean oxygen content from 234.5 ± 0.4 Pmol in 1871 to 230.6 ± 0.4 Pmol in 2010, with a sharp drop of −1402.2 ± 173.6 Tmol per decade post-1980, attributing 98.5% of total oxygen loss in the past three decades alone. Our investigation further underscored that the critical temperature at the sea surface beyond which ocean oxygen begins to rapidly decrease is approximately 18°C. Forward-looking projections underlined the potential for a decline exceeding 10% in global ocean oxygen content relative to pre-1900 levels, under uncontrolled anthropogenic carbon dioxide emissions by the end of the 21st century. These findings provide a comprehensive understanding of historical dynamics and future trajectories in global ocean oxygen, as well as a deeper knowledge of ocean deoxygenation and its underlying mechanisms.</p>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by SANER2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Three patch benchmarks included in our study. </p> <ul> <li> <p>Tian: The patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper.</p> </li> <li> <p>Wang: The patches from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</p> </li> <li> <p>Cache: The patches collected by ourselves, which is consist of 17,377 deduplicated overfitting patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and 17,377 instances from <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>(used as correct patches).</p> </li> </ul> </li> <li> <p>Results:</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em>[model]_[classifier]</em>.csv.</p> <p>For example, the file named <strong>BERT_DT.csv</strong> in the folder <strong>Tian's_dataset</strong> means that this file is the result of patches from <strong>Tian's</strong> study embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li> <p>Tian's_dataset : The detailed result files on Tian's dataset. </p> </li> <li> <p>Cache_dataset : The detailed result files on our own dataset. </p> </li> <li> <p>Cross_dataset : The detailed result files of representation learning techniques when training on our own dataset and testing on Tian's dataset.</p> </li> </ul> </li> <li>RQ2: The detailed result files in RQ2. <ul> <li> <p>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/">ASE20</a>.</p> </li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.</p> </li> </ul> </li> </ul> </li> </ul> <p>Source: The source code and lib for running Cache is available at <a href="https://github.com/APR-Study/Cache">https://github.com/APR-Study/Cache</a>.</p>
Dataset related to article "Impaired awareness in mesial temporal lobe epilepsy: Network analysis of foramen ovale and scalp EEG"
<p>XLSX: statistical results of network analysis of BESTA patients included in the study</p>
Supporting awareness in communities of learning design practice
<p>The following dataset has been used for the paper:</p> <p>Michos, K., & Hernández-Leo, D. (2018). Supporting awareness in communities of learning design practice. <em>Computers in Human Behavior, </em>85, 255-270. <a href="https://doi.org/10.1016/j.chb.2018.04.008">https://doi.org/10.1016/j.chb.2018.04.008</a></p> <p>Abstract</p> <p>The field of learning design has extensively studied the use of technology for the authoring of learning activities. However, the social dimension of the learning design process is still underexplored. In this paper, we investigate communities of teachers who used a social learning design platform (ILDE). We seek to understand how community awareness facilitates the learning design activity of teachers in different educational contexts. Following a design-based research methodology, we developed a community awareness dashboard (inILDE) based on the Cultural Historical Activity Theory (CHAT) framework. The dashboard displays the activity of teachers in ILDE, such as their interactions with learning designs, other members, and with supporting learning design tools. Evaluations of the inILDE dashboard were carried out in four educational communities – two secondary schools, a master programme for pre-service teachers, and in a Massive Open Online Course (MOOC) for teachers. The dashboard was perceived to be useful in summarizing the activity of the community and in identifying content and members’ roles. Further, the use of the dashboard increased participants’ interactions such as profile views and teachers showed a willingness to build on the contributions of others. As conclusions of the study, we propose five design principles for supporting awareness in learning design communities, namely community context, practice-related insights, visualizations and representations, tasks and community interests.</p>
Breast arterial calcifications as a biomarker of cardiovascular risk: radiologists' awareness, reporting, and action. A survey among the EUSOBI members
<p>Data set from the article Trimboli RM, Capra D, Codari M, Cozzi A, Di Leo G, Sardanelli F. Breast arterial calcifications as a biomarker of cardiovascular risk: radiologists' awareness, reporting, and action. A survey among the EUSOBI members. Eur Radiol. 2021 Feb;31(2):958-966. doi: 10.1007/s00330-020-07136-6. Epub 2020 Aug 27. PMID: 32851451; PMCID: PMC7813731.</p> <p><strong>Abstract</strong></p> <p><strong>Objectives: </strong>To investigate the knowledge of radiologists on breast arterial calcifications (BAC) and attitude about BAC reporting, communication to women, and subsequent action.</p> <p><strong>Methods: </strong>An online survey was offered to EUSOBI members, with 17 questions focused on demographics, level of experience, clinical setting, awareness of BAC association with cardiovascular risk, mammographic reporting, modality of BAC assessment, and action habits. Descriptive statistics were used.</p> <p><strong>Results: </strong>Among 1084 EUSOBI members, 378 (34.9%) responded to the survey, 361/378 (95.5%) radiologists, 263 females (69.6%), 112 males (29.6%), and 3 (0.8%) who did not specify their gender. Of 378 respondents, 305 (80.7%) declared to be aware of BAC meaning in terms of cardiovascular risk and 234 (61.9%) to routinely include BAC in mammogram reports, when detected. Excluding one inconsistent answer, simple annotation of BAC presence was declared by 151/233 (64.8%), distinction between low versus extensive BAC burden by 59/233 (25.3%), and usage of an ordinal scale by 22/233 (9.5%) and of a cardinal scale by 1/233 (0.4%). Among these 233 radiologists reporting BAC, 106 (45.5%) declared to orally inform the woman and, in case of severe BAC burden, 103 (44.2%) to investigate cardiovascular history, and 92 (39.5%) to refer the woman to a cardiologist.</p> <p><strong>Conclusion: </strong>Among EUSOBI respondents, over 80% declared to be aware of BAC cardiovascular meaning and over 60% to include BAC in the report. Qualitative BAC assessment predominates. About 40% of respondents who report on BAC, in the case of severe BAC burden, investigate cardiovascular history and/or refer the woman to a cardiologist.</p>
Host-aware RNA-based control of microbial consortia
<p>Plasmid maps are provided as Genbank files.</p>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".</p> <p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by ISSTA2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Two patch benchmarks included in our study.</p> <ul> <li>Small: The 1,183 deduplicated patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper and Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li>Large: The patches collected by ourselves, which is consist of totally 49,694 patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>.</li> </ul> </li> <li> <p>Results</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em><code>[model]_[classifier].csv</code></em>. For example, the file named <em><code>BERT_DT.csv</code></em> in the folder <em><code>Small</code></em> means that this file is the result of patches from <strong>Small</strong> dataset embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li>Small: The detailed result files on Small dataset.</li> <li>Large: The detailed result files on Large dataset.</li> <li> <p>Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.</p> </li> </ul> </li> <li> <p>RQ2: The detailed result files in RQ2.</p> <ul> <li>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.</p> </li> <li> <p>Table_5_Effectiveness_APCA.xlsx: The detailed version of <strong>Table 5</strong> in the paper.</p> </li> <li> <p>Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper.</p> </li> </ul> </li> </ul> </li> <li>Source: The source code and lib for running Cache. Guidance for replicating our study is available at <em><code>source/Readme.md</code></em>. We will build a homepage for Cache on GitHub upon acceptance.</li> </ul>
Layout-Aware Neural Model for Resolving Hierarchical Table Structure
<p>Financial tables annotated for Cell type and Column level hierarchies. These tables are to be used in tandem with IBM FinTabNet available at https://developer.ibm.com/exchanges/data/all/fintabnet/</p>
Data from: Awareness of residents about kala-azar and its related practices in two endemic areas of Bangladesh
Kala-azar, a worldwide disease, is caused by the parasitic protozoan species of genus Leishmania and transmitted by species of sandflies. Awareness of the population about the disease is essential to run a successful control-strategies program. This cross-sectional study was conducted to assess the awareness of residents about it and related practices in two highly-endemic areas of Bangladesh. In total, 511 household respondents were selected conveniently from two unions (Kushmail and Kanihari) under two sub-districts (Trishal and Fulbaria) of Mymensingh district. Interviewer-administered questionnaires were used for assessing the awareness of the respondents about kala-azar and their practices. Knowledge scores were categorized as poor (mean +1SD). Statistical tests were considered significant at p value of ≤5% (≤0.05). Chi-squared and Student's t-tests were performed for statistical analysis. The mean knowledge score of the respondents of Kanihari union regarding kala-azar was significantly higher than that of the respondents of Kushmail union (mean ±SD, 4.30 ±0.86 versus 4.12 ±0.75, p=0.002). Of the respondents, 11% and 20% had good (>5.04), 77% and 72% had average (3.43-5.04), 12% and 8% had poor (<3.43) (GAP) knowledge on kala-azar in Kushmail union and Kanihari union respectively. However, the mean knowledge score of the respondents of Kanihari union relating sandfly was significantly lower compared to that of the respondents of Kushmail union (mean ±SD, 2.49 ±0.79 versus 2.65 ±0.85, p=0.03). Of them, 22% and 19% had good (>3.39), 67% and 64% had average (3.39-1.75), and 11% and 17% had poor (<1.75) knowledge on sandfly in Kushmail union and Kanihari union respectively. More than 70% of the respondents from Kushmail and Kanihari mentioned only injection as the preferred treatment of kala-azar. Eighty-seven percent and 88% of the respondents in the two unions had chosen upazilla health complex for the treatment of their kala-azar. About 50% of the respondents in Kushmail and Kanihari liked to use coil or mat as a preventive measure to avoid sandfly bites. The findings of this study indicate the importance of awareness and related practices of the community people in two endemic areas that will help implement the kala-azar-elimination program successfully in Bangladesh.
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.