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334 results for “household”
HOWS-CL-25: Household Objects Within Simulation Dataset for Continual Learning
<p>HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly.<br> This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation.<br> Or where household objects or continual learning are of interest.</p> <p>Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image.</p> <p>The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning.<br> This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random<br> displacement texture.<br> We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline!</p> <p>For continual learning (CL), we provide two different loading schemes:<br> - Five sequences with five categories each<br> - Twelve sequences with three categories in the first and two in the other sequences.</p> <p>In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper.<br> In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart.</p> <p>We recommend using those loading schemes to compare your approach with others.</p> <p>Here we provide three files for download:<br> - HOWS_CL_25.zip [124GB]: This is the original dataset with the RGB, depth, segmentation, and normal images, as well as the loading schemes. It is divided into three archive parts. To open the dataset, please ensure to download all three parts.<br> - HOWS_CL_25_hdf5_features.zip [2.5GB]: This only contains the calculated features from the RGB input by a ResNet50 in a .hdf5 file. Download this if you want to use the dataset for learning and/or want to compare your approach to our RECALL approach (where we used the same features).<br> - README.md: Some additional explanation.</p> <p>For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.</p>
WASH in households dataset (from the Joint Monitoring Programme database). June 2019
<p>This dataset compromises all country files included in the WHO/UNICEF Joint Monitoring Programme (JMP) global database (<a href="https://washdata.org/data/household">https://washdata.org/data/household</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong><em>Country</em>:</strong> ISO 3 code + Complete name</p> <p><strong><em>Service</em>:</strong> Water or Sanitation</p> <p><strong><em>Setting</em>: </strong>Urban or Rural</p> <p><strong><em>Source</em>: </strong>Category of the household survey</p> <p><strong><em>Year</em>: </strong>Date of the household survey</p> <p><strong><em>X<sub>1</sub>, X<sub>2</sub> and X<sub>3</sub>:</em></strong> percentage of the population using…</p> <ul> <li>In <strong>Water</strong>: X<sub>1</sub> all improved drinking water sources; X<sub>2</sub> piped drinking water sources and X<sub>3</sub> no drinking water facility (surface water).</li> <li>In <strong>Sanitation</strong>: X<sub>1</sub> all improved sanitation facilities; X<sub>2</sub> improved sanitation facilities connected to sewers and X<sub>3</sub> no sanitation facilities (open defecation).</li> </ul> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Estimating access to drinking water and sanitation: The need to account for uncertainty in trend analysis. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2019.133830</em></p> <p><a href="https://doi.org/10.1016/j.scitotenv.2019.133830">https://doi.org/10.1016/j.scitotenv.2019.133830</a></p>
ENABLE.EU H2020 project dataset and questionnaire from a survey of households on energy use and energy choices
<p>The ZIP archive includes the anonymized micro-data (survey results) and the respective questionnaire from the survey of households in eleven countries, conducted as part of the H2020 project "<a href="http://www.enable-eu.com">Enabling the Energy Union through understanding the drivers of individual and collective energy choices in Europe</a>" (ENABLE.EU). </p> <p>The countries are: Bulgaria, France, Germany, Hungary, Italy, Norway, Poland, Serbia, Spain, Ukraine, and the United Kingdom.</p> <p>The dataset consists of 11 267 completed questionnaires (cases). </p> <p>The ZIP archive includes the following files:<br> • ENABLE.EU survey questionnaire for households in PDF format;<br> • ENABLE dataset from the survey of households in SAV format for IBM SPSS;<br> • ENABLE dataset from the survey of households in DTA format for STATA (the dataset is produced by simple export from SAV format and could contain some differences due to export limitations; If possible, we recommend to use the SAV-SPSS format);<br> • ENABLE dataset from the survey of households in XLSX format for Microsoft Excel, which includes also corresponding tables for the labels of questions and answers.</p> <p>For more information about the survey methodology and survey results please see: "D4.1 Final report on comparative sociological analysis of the household survey results" under the section <a href="http://www.enable-eu.com/downloads-and-deliverables/">Downloads / Deliverables</a> at the ENABLE.EU web-site. </p>
Perceptions of green facades among residents of buildings with and without a greened envelope – Data from a household survey in Leipzig, Germany
<p>The data set stems from a survey of residents in two neighborhoods of Leipzig, Germany, and was implemented in April and May of 2022. The primary aim of the study was to better understand resident perceptions of green facades, including their (perceived) benefits as well as concerns. Additionally, residents were asked for a number of other perceptions, including heat stress, noise and air pollution. The sample includes both residents of buildings with and without an existing green facade.</p> <p>All variables included in this data publication are described in the codebook. The original German language wording of the survey questions can be found in the questionnaire enclosed with the data set. We include responses to all questions from the survey that were close-ended or had a numerical response. Open-ended questions were excluded from this publication for data privacy reasons. </p>
SHEERM: Sustainable Household Energy and Environment Resources Management dataset
<p>This dataset represents a novel and extensive dataset featuring comprehensive cross-sectional data of household electrical load, energy cost, and on-premises solar energy production, directly linked to solar radiation and weather parameters. <br>The SHEERM dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SGD) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation.<br>Together with the SHEERM dataset, there is a paper that details the data collection process, including the sources and methodologies employed. Adhering to established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying various machine-learning approaches to the provided data.<br>The SHEERM dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition towards renewable energy and carbon neutrality.</p>
Lietuvos namų ūkių apklausa energetikos klausimais (Lithuanian Household Survey on Energy Issues)
<p>Duomenų rinkinyje pateikiami reprezentatyvios Lietuvos namų ūkių apklausos (N=1008) apie Lietuvos namų ūkių energetikos situaciją ir su valstybės parama šioje srityje susijusias žinias. Apklausos klausimyną parengė Lietuvos energetikos instituto mokslininkai, o apklausos lauko darbus atliko UAB "Vilmorus" 2020 m. lapkričio mėn. 16 d. – gruodžio mėn. 7 d.</p> <p> </p>
Lithuanian Household Energy Expenditure and Energy Poverty Data, 2019
<p>This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.</p> <p>Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.</p>
Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households
<p><strong>Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households</strong></p> <p> </p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p>
SARS-CoV-2 Infection and Clinical Signs in Cats and Dogs from Confirmed Positive Households in Germany
<p>Supplemental material and raw data referring to specified publication</p>
Baltimore Ecosystem Study: Household Telephone Survey in support of Locke et al 2019 in PLoS One
This is a subset of the data found in Grove and Locke (2018), to be included with: Locke, D.H., Polsky, C., Grove, J. M., Groffman, P. M., Nelson, K.C., Larson, K. L., Cavender-Bares, J., Heffernan, J. B., Roy Chowdhury, R., Hobbie, S. E., Bettez, N., Neill, C., Ogden, L.A., O’Neil-Dunne, J. P. M.. [accepted]. Heterogeneity of practice underlies the homogeneity of ecological outcomes of United States yard care in metropolitan regions, neighborhoods and households. PLoS ONE doi:10.1371/journal.pone.0222630 These data contain answers 2011 survey questions: In the past year, which of the following has been applied to any part of your yard: Water for irrigating grass, plants, or trees? Fertilizers? Pesticides to get rid of weeds or pests? The total household annual income (8 ordinal categories), age of respondent (5 ordinal categories), and the answer to: About how many neighbors do you know by name? (recorded in 5 ordinal categories). Two additional columns are provided to indicate the metropolitan region of the respondent (one of the following six: Phoenix, Los Angeles, Minneapolis - St. Paul, Baltimore, Boston, or Miami) and the degree of urbanicity in that region (Urban, Suburban, or Exurban). See Grove and Locke 2018 for additional details. This research is supported by the Macro- Systems Biology Program (US NSF) under Grants EF-1065548, -1065737, -1065740, -1065741, -1065772, -1065785, -1065831, and -121238320 and the NIFA McIntire-Stennis 1000343 MIN-42-051. The work arose from research funded by grants from the NSF LTER program for Baltimore (DEB- 0423476, DEB-1027188); Phoenix (BCS-1026865, DEB-0423704, DEB-9714833, DEB-1637590, DEB-1832016); Plum Island, Boston (OCE-1058747 and 1238212); Cedar Creek, Minneapolis–St. Paul (DEB- 0620652); and Florida Coastal Everglades, Miami (DBI-0620409). Edna Bailey Sussman Foundation, Libby Fund Enhancement Award and the Marion I. Wright ‘46 Travel Grant at Clark University, The Warnock Foundation, the USDA Forest Service No
Role of residential air circulation and cooling for universal household electrification
<p>This repository contains the data to replicate the analysis of the paper 'Role of residential air circulation and cooling for universal household electrification' by Giacomo Falchetta. </p> <p>Replication code and instruction are hosted at <a href="https://github.com/giacfalk/cooling_electrification">https://github.com/giacfalk/cooling_electrification</a></p> <p>To replicate the analysis:</p> <ol> <li>Clone the Github repository on your machine</li> <li>Download the Zenodo archive and extract it in the home folder of the cloned Github repository</li> <li>Ensure Python (Anaconda 3) and R 3.5.1+ are installed on your local machine</li> <li>Run the 'wrapper.R' file.</li> </ol> <p> </p>
HIFDA - High-Frequency Electrical Signals from Household Appliances Dataset
<p>This work aims to provide a new dataset containing high-frequency steady-state electrical signals from individual common household appliances. A total of 14 appliances were captured with an acquisition rate of 100 kSPS, capturing only the times when the target appliance was active and consuming power (no idle states). Also, the empty grid (no active appliance) was captured in the same conditions.</p> <p>For the creation of this dataset, 50 windows of the voltage and current signals were taken from each of the 14 isolated devices and the empty grid, each window containing approximately 54000 samples acquired at 100 kSPS, implying windows of 5.4 seconds. For convenience, the windows were split again to create three additional datasets, containing time slots of 10.24, 163.84 and 1310.72 milliseconds, in order to test different architectures when used for training the target neural network. The size of these windows has been set so that the number of data contained in each window is a power-of-two figure, seeking to create windows containing at least time slots comprising 10 ms, 100 ms and 1000 ms at 100 kSPS, respectively. This decision was made to increase the performance of the processing device that uses this data for its intended purpose.</p> <p>The dataset provided contains five main folders. One of this folders, "0.Img_Appliances", contains pictures of the 14 appliances whose electrical signals were captured to create this dataset. The rest of the folders correspond to the different window divisions, as explained before. Inside each of them there is a "Current" and a "Voltage" folder where, inside each, there is a folder for every appliance that was captured, containing the data in multiple files with text (.txt) format. It is important to keep in mind that, even though the file quantity is greater on the smaller window datasets, the information that all of the datasets contain is mostly the same, as it all comes from the full time records dataset. The bandwidth of the captured voltage signal ranges from 300 Hz to 50 kHz, so the fundamental component of the grid, located at 50 Hz, does not appear on the captured data. The captured current signal, on the other hand, has a bandwidth ranging from 30 Hz to 50 kHz approximately.</p> <p>The appliances included are:</p> <p>- Air conditioner<br>- Charger<br>- Computer<br>- Hair dryer<br>- Empty grid<br>- Griddle<br>- Heater<br>- Iron<br>- Coffee maker<br>- Laptop<br>- Light<br>- Microwave<br>- Monitor<br>- Vacuum<br>- Washing machine</p>
An epidemiological Study to Assess Household Transmission & Associated Risk Factors for COVID-19 Disease amongst Residents of Delhi, India.
<p><strong><em>Executive summary</em></strong>: Studying the spread and epidemiological characteristics of COVID-19 virus specially in household settings are needed to prepare our self-better in preventing and controlling this epidemic. In this study we proposed a conceptual framework of four level of determinates and tried to understand the transmission dynamics of COVID-19 among household contacts along with clinical, epidemiological and virologic characteristics of the infection. </p> <p><strong>Aims & Objectives:</strong></p> <ol> <li>the proportion of asymptomatic cases and symptomatic cases;</li> <li>the incubation period of COVID-19 and the duration of infectiousness and of detectable shedding;</li> <li>the serial interval of COVID-19 infection; </li> <li>clinical risk factors for COVID-19, and the clinical course and severity of disease; </li> <li>high-risk population subgroups;</li> <li>the secondary infection rate and secondary clinical attack rate of COVID-19 infection among household contacts; and</li> <li>the associations of various factors across four dimensions interaction associated with risk of transmission</li> </ol> <p><strong>Methodology:</strong> This was a case-ascertained study where all susceptible contacts of a laboratory confirmed COVID-19 case were studied prospective for four weeks after their enrolment. It was done in New Delhi, during the end of first wave as well as whole second wave from December 2020 to July 2021. The study team collected the key information by questionnaire along with blood and oro-nasal swab during the household visits. Follow-up was done on day 7, 14 and 28 for observing the disease characteristic and symptomatology along with confirmation by serum and oro-nasal swab testing. Daily characteristics of the infection were noted by the participants on symptoms diary.</p> <p><strong>Results: </strong>We enrolled 99 households, each having one laboratory-confirmed COVID-19 index case along with their 318 susceptible contacts. By the end of the follow-up, secondary infection rate was seen at 55.5%, while seroconversion in 46.6%. Hospitalization and case fatality rate was 3.83% and 1.7% respectively. Among epidemiological characteristics we observed serial interval of 8.0 ± 6.7 days, generation time 3.8 ± 6.4, while secondary attack rate was 54.9%. The predictors of secondary infection among individual contact level were being female (OR:2.13, 95% CI:1.27 - 3.57), age of the household contact (1.01;1.00 - 1.03), symptoms at baseline (3.39; 1.61- 7.12) and during follow-up (3.18; 1.64 - 6.19), while only symptoms during follow-up (3.81: 1.43 - 10.14) and being RT-PCR positive (8.32; 3.22 -21.54) was significantly and independently associated with seroconversion among household contacts. Among index case-level age of the primary case (1.03; 1.01 -1.04) and any symptoms during follow-up (6.29; 1.83-21.63) significantly and independently associated with secondary infection while any symptoms during follow-up was associated with seroconversion among household contacts. Among household-level characteristics having more rooms (4.44; 2.16 - 9.13) independently associated with secondary infection, while more rooms (3.98; 1.23 -12.90) along with overcrowding (0.37; 0.16 - 0.82) associated with seroconversion. Among contact pattern only taking care of the index case (2.02;1.21- 3.38) was significantly and independently associated with secondary infection, while none was associated with seroconversion.</p> <p><strong>Conclusion: </strong>A high secondary cases and secondary attack rate was seen in our study. This highlights the need to adopts strict measure and advocate COVID appropriate behaviours in order to break the transmission chain at household level. The targeted approach at household contacts with higher risk would be efficient in limiting the development of infection among susceptible contacts. </p>
Categorical variables based on cross country household survey on energy consumption
<p>The data used in this file was collected via two large-scale surveys conducted in Italy, Switzerland and the Netherlands. A total of 6,138 responses were recorded, containing information on socio-demographic and socio-psychological characteristics, dwelling and household characteristics, technologies and energy services used, and their metered electricity consumption. There were a large number of missing responses for metered electricity consumption in the Netherlands, leading to an under-representation of data from this country. The survey responses were used to construct newly defined energy efficiency indicators, and energy service indicators. This allows two distinct factors to be separated: service consumption, and energy efficiency relative to the demanded service. Firstly, dwelling characteristics and survey responses related to energy services (e.g. floorspace, ownership of specific appliances and number of lightbulbs), were regressed to the collected metered electricity data. For each household, this allowed us to calculate the expected lighting and appliance electricity demand based on the level service that the household demanded, which is referred to as lighting and appliance service demand indicators. The idea is that a larger house, or a house with more appliances for example is expected to use more electricity. Relative to this expected electricity demand energy efficiency can be calculated. All variables are categorised in categorical variables deducted based on the questions asked in the two surveys. The survey responses were clustered based on the lighting service demand, appliance service demand and the efficiency gap (k-means clustering with Jaccard dissimilarity measure) which is described in Edelenbosch, Miu et al (2022). Translating observed household energy behaviour to agent-based technology choices in an integrated modelling framework. <em>Iscience</em> (accepted).</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>
Estimating household preferences for coastal flood risk mitigation policies under ambiguity
<p>Risk mitigation policies (like dike rising) are essential to address increasing coastal flood risks due to global warming. Furthermore, the optimal level of risk mitigation policy should be determined by public preferences for risk reduction. However, it is difficult to reveal public preferences for coastal flood risk reduction because projections of coastal flood risks inevitably involve uncertainty. This study aims to estimate household preference for coastal flood reduction under ambiguity and multiple projections of coastal flood risks. By coupling storm surge inundation simulations and stated preference experiments with decision models, we estimate the expected loss reduction, risk premium, and ambiguity premium for coastal flood risk mitigation policies. Results of the study show that ignoring the ambiguity premium causes significant undervaluation of coastal flood risk mitigation, and the ambiguity premium stems from households' over-concern about the worst projection, which may lead to an over-allocation of resources to prevent inundation damage caused from the worst-case flood before a disaster. The study concludes that a risk mitigation policy combining public insurance for the worst projection and pre-disaster prevention measures can be effective and efficient.</p>
Analysis of public transport in Vienna with Co2 emissions in Austria's households
<p>This repository serves as a backup and longterm storage for the datasets and plots resulting from the analysis on means of transport in Vienna in the time period 2010 - 2021 together with the Co2 emissions of Austria's households.</p>
[Supporting Information] Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021
<p>Supporting information from the manuscript: <em>Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021</em>. The main goal of this study was to comprehensively analyze the evolution in diet quality in Peru in the period 2008-2021 based on apparent household purchases extracted from the National Household Survey (ENAHO, by its acronym in Spanish). Furthermore, this study identified patterns in the temporal and spatial variability of food consumption, differences in consumption based on poverty levels, and gaps in achieving consumption levels of macronutrients and calories recommended by international nutritional authorities.</p> <p>dataset1: contains the consumption of 96 food products in kg/person/year per household, for a time horizon from 2008 to 2021.</p> <p>dataset2: contains the caloric and macronutrient content in kcal or g macronutrient per 100g of 92 food items.</p> <p>dataset3: contains the consumption of calories, and macronutrients in g/person/day per household, for a time horizon from 2008 to 2021.</p> <p>dataset1_labels: contains the data dictionary of dataset1</p> <p>dataset2_labels: contains the data dictionary of dataset2</p> <p>dataset3_labels: contains the data dictionary of dataset3</p> <p> </p>
Dataset: E-Home Household Service Holdings Limited (EJH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in A new household ant record for Turkish Thrace [Monomorium pharaonis (L.)] (Hymenoptera, Formicidae)
Fig. 2: Monomorium pharaonis (LINNAEUS 1758). Worker: (a) Head (Frontal view), (b) Thorax, petiole, postpetiole (in profile); queen: (c) Head (Frontal view), (d) Thorax, petiole, postpetiole (in profile).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.