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97 results for “Nairobi”
Nairobi and Istanbul Multi-Hazard Interrelationships Database
<p><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em></p> <p><strong><span>10.5281/zenodo.13220740</span></strong></p> <p><span>This </span><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em><span> </span><span>uses a critical review of 135 sources (academic and grey literature, databases, online and social media), to identify <strong>the breadth of natural hazard types</strong> that might influence Nairobi (19 possible natural hazard types) and Istanbul (23 hazard types). We further identified <strong>hazard interrelationship pairs</strong> (e.g., an earthquake triggering landslides) in Nairobi (88 potential hazard interrelationship pairs) and Istanbul (105 hazard pairs) out of a possible 576 interrelationships. This extensive Excel (140 kb) database accompanies the paper Šakić Trogrlić et al. (2024).</span></p> <p><span>The <em>Nairobi and Istanbul Multi-Hazard Interrelationships Database</em> consists of the following eight tabs (in brackets the number of rows [R] × columns [C] of information):</span></p> <ul> <li><span>Excel Tab A. <span> </span>Single Hazard Evidence Nairobi (87R×16C)</span></li> <li><span>Excel Tab B. <span> </span>Single Hazard Evidence Istanbul (68R×11C)</span></li> <li><span>Excel Tab C. <span> </span>Hazard Interrelationships Nairobi (118R×14C)</span></li> <li><span>Excel Tab D. <span> </span>Hazard Interrelationships Istanbul (122R×13C)</span></li> <li><span>Excel Tab E. <span> </span>Definitions (Evidence Types) (7 definitions)</span></li> <li><span>Excel Tab F. <span> </span>Definitions (Hazards) (31R×5C)</span></li> <li><span>Excel Tab G. <span> </span>Definitions (Hazard Relations) (3 definitions)</span></li> <li><span>Excel Tab H. <span> </span>References</span></li> </ul> <p><span> </span><span>For <span>Nairobi (Tab A) and Istanbul (Tab B)</span>, each row in the database presents a source of evidence of a <strong>single hazard type</strong> influencing Nairobi or Istanbul. We compiled multiple evidence sources for many of the hazard types, each on its own row. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>identifying the hazard type (24 possible hazard types)</span></li> <li><span>source information and URL link</span></li> <li><span>source content</span></li> <li><span>hazard interrelationships</span></li> <li><span>anthropogenic influences</span></li> <li><span>video evidence</span></li> <li><span>source reflections</span></li> </ul> <p><span> </span><span>For <span>Nairobi (<strong>Tab C</strong>) and Istanbul (<strong>Tab D</strong>)</span>, each row in the databases presents a source of evidence of a <strong>hazard interrelationship</strong> in Nairobi or Istanbul. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>primary hazard (24 hazard types)</span></li> <li><span>secondary hazard (where applicable, the same 24 hazards as for the primary hazard)</span></li> <li><span>the generic description of hazard interrelationship mechanisms</span></li> <li><span>whether the relationship is triggered or increased probability or both</span></li> <li><span>source information and URL link</span></li> <li><span>source content (e.g., interrelationship type, description, and hazard sequence)</span></li> </ul> <p><span>The reader is referred to Šakić Trogrlić et al. (2024) for a detailed description of the methodology by which this database was constructed.</span></p> <p><strong><span>References</span></strong></p> <p><a name="_Hlk154927256"></a><span>Šakić Trogrlić, R., Thompson, H. E., Yahya Menteşe, E., Hussain, E., Gill, J. C., Taylor, F. E., Mwangi, E., Öner, E., Bukachi, V. G., & Malamud, B. D. (2024). Multi-hazard interrelationships and risk scenarios in urban areas: A case of Nairobi and Istanbul. <em>Earth’s Future.</em> 12, e2023EF004413. https://doi.org/10.1029/2023EF004413</span></p>
Household surveys in four informal settlements in Abidjan (Côte d'Ivoire) and Nairobi (Kenya)
<p><strong>Description:</strong> Latest release of data (anonymized) collected in informal settlements in Côte d'Ivoire and Kenya during my PhD thesis, with the respective metadata (questionnaire files). Please note that some data (geolocation, specific age of participant, and health facilities used) have been ommitted due to personal data protection concerns.</p> <p><strong>Includes:</strong> Data (CSV), questonnaires (XLS), and Jupyter notebooks summarizing the data (using Python).</p> <p><strong>Ethical clearance:</strong> We obtained ethical clearance in Switzerland from EPFL’s HREC (decision n° 068-2020), in Kenya from KEMRI (KEMRI/RES/7/3/1) and the National Commission for Science, Technology & Innovation (NACOSTI/P/21/10921), and in Côte d’Ivoire from the National Health and Life Sciences Ethics Committee (Comité National d’Éthique des Sciences de la Vie et de la Santé, ref. n° 005-22/MSHPCMU/CNESVS-km).</p> <p><strong>Citation:</strong> Pessoa Colombo V. Relating health benefits of water, sanitation, and hygiene services with the context of urban informal settlements: lessons from Côte d'Ivoire and Kenya. PhD thesis. EPFL: Lausanne. 2023. https://doi.org/10.5075/epfl-thesis-10143</p>
Nairobi_Street_Trees_Distribution_Diversity
<p>Input data and code to accompany the paper:</p> <p>Alice Gerow, Vivian Kathambi, Dexter Locke, Mark Ashton, Craig Brodersen. Street tree communities reflect socioeconomic inequalities and legacy effects of colonial planning in Nairobi, Kenya. Urban Forestry & Urban Greening. <a href="https://doi.org/10.1016/j.ufug.2024.128530">https://doi.org/10.1016/j.ufug.2024.128530</a></p> <p>The input data consists in street tree observations collected during a field survey conducted between June and August 2023 in Nairobi, Kenya. The code includes descriptive tables and plots, statistical tests, and alpha and beta diversity metrics and visualizations used to compare ecological communities across social groups.</p>
Household Survey in Nairobi (Kibera & Eastleigh) for the "Urban Waterscapes and the Pandemic" research project
<p><strong>"Urban Waterscapes and the Pandemic" research project:</strong> The Covid-19 pandemic has brought to the fore the importance of water access as an essential service protecting human health. Yet, the prevention of human-to-human transmission of the novel virus may be impacted by uneven geographies of water access. The pandemic presented a dilemma in water-deprived urban areas as residents needed to find ways to adapt to new hygiene standards and local Covid-19 responses. Focusing on Nairobi, Kenya’s capital with historically uneven and highly contested geographies of water, we mobilized the concept of waterscapes in order to understand how Nairobi’s waterscapes have changed during the pandemic; how these waterscape changes relate to new requirements; and how far they reflect adaptive creativity or re-produce urban fragmentation. Funded by DFG, the project is a 12-month-long collaboration between IPS, the Department of Urban and Regional Planning at the University of Nairobi, and the British Institute for Eastern Africa.</p> <p><strong>Household survey in Kibera and Eastleigh:</strong> As part of the "Urban Waterscapes and the Pandemic" research project, the project team conducted a household survey in two target areas of Nairobi, namely Kibera and Eastleigh. The survey was conducted in April and May 2022 with the support of 11 enumerators. Spread purposefully over four sub-locations in each target area, the survey included more than 400 respondents per area. The final data set has been quality-checked and cleaned for further analysis; personal details about the respondents and the enumerators that may reveal their identity have been removed.</p>
Linked collectors and determiners for: Occurrence of Buprestidae housed at the natural history museum collection in Nairobi, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Occurrence of Buprestidae housed at the natural history museum collection in Nairobi, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/79108e77-a8d7-4562-9427-d5d6c4d7decd">https://bionomia.net/dataset/79108e77-a8d7-4562-9427-d5d6c4d7decd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/79108e77-a8d7-4562-9427-d5d6c4d7decd">https://gbif.org/dataset/79108e77-a8d7-4562-9427-d5d6c4d7decd</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence data of Elateridae housed in the natural history museum collection in Nairobi, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Occurrence data of Elateridae housed in the natural history museum collection in Nairobi, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988">https://bionomia.net/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988">https://gbif.org/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence of invertebrates from all over Africa in natural history museum collection in Nairobi, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Occurrence of invertebrates from all over Africa in natural history museum collection in Nairobi, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3b8eb92e-2967-42a7-9027-2351e6956813">https://bionomia.net/dataset/3b8eb92e-2967-42a7-9027-2351e6956813</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3b8eb92e-2967-42a7-9027-2351e6956813">https://gbif.org/dataset/3b8eb92e-2967-42a7-9027-2351e6956813</a>. Formatted as a Frictionless Data package.
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
<p><strong><span>Background</span></strong></p> <p><span>Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in <em>Escherichia</em> <em>coli</em>, regarded as a sentinel organism.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced <em>E</em>. <em>coli</em> isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock.</span></p> <p><strong><span>Findings</span></strong></p> <p><span>We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.</span></p>
Travel Times by Transportation Mode in Nairobi, Kenya
<p>This directory contains data files that describe travel times in<br> Nairobi, Kenya. Three different modes of transportation are included:</p> <p> - Walking, according to MapQuest and filled in with Google Maps where<br> MapQuest fails to find a route.</p> <p> - Driving, under normal traffic conditions.</p> <p> - Matatus, the informal public transportation network.</p> <p>The travel times are computed from a gridded origin location to a<br> collection of gridded destination locations. In the directories, each<br> file contains destination location times for a given origin location.<br> These are titled <mode>-###.csv. The CSV is organized from north to<br> south, along rows, and west to east, along columns. An additional<br> <mode>.csv file in each directory gives the organization of the<br> numbered files, again from north to south, along rows, and west to<br> east, along columns.</p> <p>There are also "extended" versions of each grid, with larger<br> destination grids for each origin point, formed by "daisy-chaining"<br> together the original destination grids.</p> <p>The directory also contains source files for working with the data:</p> <p> - calc_access.R shows how to calculate a simple mobility measure,<br> using the functions in tools_calc_access.R</p> <p> - tools_analyze.R are a more general set of functions for performing<br> arbitrary callback-based operations on the travel time data.</p> <p> - tools_calc_access.R contains underlying functions for setting up<br> access to the grids and calculating access.</p> <p> - tools_map.R contains the grid origin and destination definitions<br> and functions for plotting.</p>
Risky sexual behaviours and utilization of HIV testing services among the adolescent girls and young women aged between 15-24 years in Kibra Sub County, Nairobi County, Kenya
<p><strong>Introduction: </strong>HIV remains a significant global health challenge, disproportionately affecting adolescent girls and young women (AGYW). HIV testing is crucial in controlling the infection and reducing its prevalence. Understanding risky sexual behaviors among AGYW is pivotal in aligning prevention interventions. Despite global prevention efforts, testing gaps persist among AGYW, linked to risky sexual behavior (RSB). This study explores the association between these behaviors and HIV testing utilization among AGYW (aged 15-24) in Kibra Sub County, Nairobi.</p> <p><strong>Methods: </strong>A cross-sectional study sampled 379 AGYW from three wards in Kibra Sub County in Nairobi County. To be an eligible participant, one must have been a resident for at least one year before the time of the study and aged between 15-24 years, employing standardized structured interviewer-administered questionnaires and statistical analyses. Results were analyzed using Chi-square tests and logistic regression. Data was collected between June to July 2023.</p> <p><strong>Results: </strong>Overall, HIV testing prevalence was 60.7% (n=230). Those aged 20-24 were 71.3% (n=164), with secondary education were 63.5% (n=146) and married 28.7% (66) were more likely to undergo testing. Participants engaging in risky behaviors such as lack of condom use (3.96 times more likely), experiencing gender-based violence (4.65 times more likely), or contracting STIs (2.85 times more likely) had higher odds of seeking HIV testing services.</p> <p><strong>Conclusion: </strong>This study establishes a clear link between risky sexual behaviors and HIV testing among AGYW, with a 60.7% testing prevalence; however, gaps still exist. Efforts to enhance testing rates are vital. Interventions should align with acceptable methods, focusing on this high-risk group to ensure effective HIV care and prevention.</p>
Financial, supplies, and human resource preparedness in management of COVID-19 pandemic among dental facilities in Nairobi County, Kenya
<p>The COVID-19 pandemic put a strain on healthcare facilities across the globe. Dental facilities pose the highest COVID-19 transmission risk categories due to the aerosol-generating procedures involved in dental practice. This study aimed to determine financial, supplies, and human resource preparedness in managing the COVID-19 pandemic among Nairobi County, Kenya dental facilities. A cross-sectional study was conducted using a mixed-methods approach among 183 dental facilities in Nairobi County. Data was collected using a KoboCollect questionnaire and analyzed using MS Excel and SPSS version 26. Dental facilities' readiness was assessed using the ReadyScore Criteria. Qualitative data was collected through one-on-one interviews with key informants and analyzed thematically. Readyscore Criteria analysis showed that 39 (21.3%) of the evaluated dental facilities were considered "ready," while 133 (72.7%) and 11 (6%) were considered to have "work to do" and "not ready" for the pandemic. Bivariate analysis showed that the level of facility (p<0.001), presence of other departments (p<0.001), funds sufficiency for COVID-19 emergency response (p=0.001), and clients attended per month (p=0.017) were statistically significant factors associated with pandemic preparedness scores. Regression analysis revealed that the presence of other departments among the dental facilities was a significant predictor of readiness, with a 4.5 times higher likelihood of being ready for a pandemic (aOR 4.591; 1.471–14.327, p=0.009) compared to other facilities. Support from healthcare authorities and capacity-building initiatives are recommended to enhance preparedness and resilience among dental facilities in the face of the COVID-19 pandemic.</p>
Contextualization of enablers and barriers for COVID-19 vaccine uptake among adult tuberculosis patients attending selected clinics in Nairobi County, Kenya
<p>Although vaccination is a cost-effective, equitable, and impactful public health intervention in curbing the spread of infectious disease, low uptake is a significant concern, especially among high-risk population groups. Nearly half of the population is unvaccinated in Nairobi, yet there is a shortage of vaccination information on vulnerable tuberculosis (TB) patients. The interplay of factors influences uptake, and protecting this vulnerable group and the general population from severe disease, hospitalization, and deaths is worthy. The purpose of this study is to determine the prevalence and individual-level enablers and barriers to COVID-19 vaccine uptake among adult TB patients attending selected clinics in Nairobi County, Kenya. This cross-sectional mixed-method study was conducted at TB clinics across six sub-counties in Nairobi County. It included 388 participants sampled from each clinic's TB register. Quantitative data was collected using a questionnaire, and qualitative data was collected through key informant interviews and focus group discussions. Quantitative data was analyzed using descriptive statistics (frequencies and percentages for categorical variables and mean standard deviation for continuous variables) and inferential statistics (logistic regression). Qualitative data was analyzed through deductive coding and thematic analysis. The prevalence of COVID-19 vaccination was 46.1%, with 38.1% receiving complete vaccination. Mistrust in vaccine management (adjusted odds ratio (aOR)= 0.075, 95% confidence interval (CI): 0.025-0.229, <em>p </em><0.001) was a significant barrier to COVID-19 vaccine uptake. Perceived covid-19 susceptibility (aOR = 2.901, 95% CI: 1.258-6.688, <em>p </em>= 0.012) and perceived covid-19 seriousness (aOR = 3.294, 95% CI: 1.130-9.604, <em>p </em>= 0.029) were significant enablers of COVID-19 vaccine uptake. Qualitative themes related to individual-level barriers and enablers of COVID-19 vaccine uptake were fear of side effects, stigma, myths, and mistrust in the messaging for barriers and desire to protect others and risk perception as enablers. The study revealed critical individual-level factors related to COVID-19 vaccine uptake.</p>
MEASURING EFFICIENCY AND BENCHMARKING CLASSIFIED TWO - FIVE STAR HOTELS IN NAIROBI AND MOMBASA, KENYA
<p>The purpose of this study was to measure the relative efficiency of the hotels in Nairobi and Mombasa using Data Envelopment Analysis. The study was a longitudinal survey in which data are collected for periods; 2007, 2008 and. The study was limited to two-five star hotels. The study sample consisted of 36 hotels. The results revealed that technical inefficiencies of the hotels were mainly due to the pure technical inefficiencies rather than the scale inefficiencies.</p>
DATA ON HYGIENIC KNOWLEDGE AND PRACTICES OF MILK VENDING MACHINE HANDLERS IN THE INFORMAL SETTLEMENTS OF NAIROBI KENYA
<p>Data used in the research development on hygienic knowledge and practices of handlers in the informal settlements of Nairobi. </p>
Deprived Urban Areas in Nairobi
<p>Code and main results of the analysis in the deprived urban areas of Nairobi, Kenya through Earth Observation and machine learning techniques.</p>
ED3054 C. briggsae Wild Isolate (Nairobi, Kenya) | 2011-03-30T15:46:50+01:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=3MMelt8vypI</li> <li><b>strain</b> : ED3054</li> <li><b>timestamp</b> : 2011-03-30T15:46:50+01:00</li> <li><b>gene</b> : -N/A-</li> <li><b>chromosome</b> : -N/A-</li> <li><b>allele</b> : -N/A-</li> <li><b>strain_description</b> : C. briggsae Wild Isolate (Nairobi, Kenya)</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : 507 ED3054 on food L_2011_03_30__15_46_50___8___9</li> <li><b>total time (s)</b> : 684.794</li> <li><b>frames per second</b> : 28.7356</li> <li><b>video micrometers per pixel</b> : 4.52754</li> <li><b>number of segmented skeletons</b> : 15519</li> </ul>
ED3054 C. briggsae Wild Isolate (Nairobi, Kenya) | 2011-03-17T12:40:49+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=6Ms-yY7Zyck</li> <li><b>strain</b> : ED3054</li> <li><b>timestamp</b> : 2011-03-17T12:40:49+00:00</li> <li><b>gene</b> : -N/A-</li> <li><b>chromosome</b> : -N/A-</li> <li><b>allele</b> : -N/A-</li> <li><b>strain_description</b> : C. briggsae Wild Isolate (Nairobi, Kenya)</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : 507 ED3054 on food L_2011_03_17__12_40_49___8___5</li> <li><b>total time (s)</b> : 898.967</li> <li><b>frames per second</b> : 30.03</li> <li><b>video micrometers per pixel</b> : 4.52754</li> <li><b>number of segmented skeletons</b> : 26826</li> </ul>
ED3054 C. briggsae Wild Isolate (Nairobi, Kenya) | 2011-03-04T12:20:44+00:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=CX0x3L0-Gmw</li> <li><b>strain</b> : ED3054</li> <li><b>timestamp</b> : 2011-03-04T12:20:44+00:00</li> <li><b>gene</b> : -N/A-</li> <li><b>chromosome</b> : -N/A-</li> <li><b>allele</b> : -N/A-</li> <li><b>strain_description</b> : C. briggsae Wild Isolate (Nairobi, Kenya)</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : unknown</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Andre Brown</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : 507 ED3059_2011_03_04__12_20_44___8___5</li> <li><b>total time (s)</b> : 898.967</li> <li><b>frames per second</b> : 30.03</li> <li><b>video micrometers per pixel</b> : 4.52754</li> <li><b>number of segmented skeletons</b> : 26893</li> </ul>
ED3054 C. briggsae Wild Isolate (Nairobi, Kenya) | 2011-03-28T15:12:51+01:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=fUK97O4Zzmg</li> <li><b>strain</b> : ED3054</li> <li><b>timestamp</b> : 2011-03-28T15:12:51+01:00</li> <li><b>gene</b> : -N/A-</li> <li><b>chromosome</b> : -N/A-</li> <li><b>allele</b> : -N/A-</li> <li><b>strain_description</b> : C. briggsae Wild Isolate (Nairobi, Kenya)</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : 507 ED3054 on food L_2011_03_28__15_12_51___8___5</li> <li><b>total time (s)</b> : 898.967</li> <li><b>frames per second</b> : 30.03</li> <li><b>video micrometers per pixel</b> : 4.52754</li> <li><b>number of segmented skeletons</b> : 26636</li> </ul>
ED3054 C. briggsae Wild Isolate (Nairobi, Kenya) | 2011-04-12T12:32:45+01:00
<blockquote> <p>This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org</p> </blockquote> <ul> <li><b>preview link</b> : https://www.youtube.com/watch?v=JObqTkfoh9A</li> <li><b>strain</b> : ED3054</li> <li><b>timestamp</b> : 2011-04-12T12:32:45+01:00</li> <li><b>gene</b> : -N/A-</li> <li><b>chromosome</b> : -N/A-</li> <li><b>allele</b> : -N/A-</li> <li><b>strain_description</b> : C. briggsae Wild Isolate (Nairobi, Kenya)</li> <li><b>sex</b> : hermaphrodite</li> <li><b>stage</b> : adult</li> <li><b>ventral_side</b> : clockwise</li> <li><b>media</b> : NGM agar low peptone</li> <li><b>arena</b> : <ul> <li><b>style</b> : petri</li> <li><b>size</b> : 35</li> <li><b>orientation</b> : away</li> </ul> </li> <li><b>food</b> : OP50</li> <li><b>habituation</b> : 30m wait</li> <li><b>who</b> : Laura Grundy</li> <li><b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.</li> <li><b>lab</b> : <ul> <li><b>name</b> : William R Schafer</li> <li><b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK</li> </ul> </li> <li><b>software</b> : <ul> <li><b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)</li> <li><b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219</li> <li><b>featureID</b> : @OMG</li> </ul> </li> <li><b>base_name</b> : 507 ED3054 on food L_2011_04_12__12_32_45___8___5</li> <li><b>total time (s)</b> : 898.967</li> <li><b>frames per second</b> : 30.03</li> <li><b>video micrometers per pixel</b> : 4.52754</li> <li><b>number of segmented skeletons</b> : 26630</li> </ul>
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.