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Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
Improving Artificial Teachers by Considering How People Learn and Forget: Dataset
<p>This dataset contains the results of the experiment described in <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>. </p> <p>This dataset contains 4 data files:</p> <ul> <li><em>data.csv</em>: the main data file.</li> <li><em>stimuli.csv:</em> the description/listing of the stimuli.</li> <li><em>demographic_info.csv</em>: the demographic information about the users.</li> <li><em>data_incl_preliminary_exp.csv</em>: an additional data file that includes the user of the preliminary experiments</li> </ul> <p>The main data file contains the logs of 53 different users using a self-teaching application for one week. The goal of the users was to learn the English meaning of Japanese kanji. Each user completed between 1370 trials and 1608 trials. Each user saw between 85 and 204 characters. </p> <p>Two additional files are also joint to the data files:</p> <ul> <li><em>info.ipynb</em>: A Jupyter notebook that provides information about each data file, a few descriptive plots, and an example of data manipulation.</li> <li><em>info.pdf: </em>A pdf rendering of the notebook.</li> </ul> <p>If you use this dataset, please refer to it by citing <a href="https://dl.acm.org/doi/10.1145/3397481.3450696">Nioche et al. (2021)</a>.</p>
THOR - people tracks
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p>The dataset includes 13 separate recordings in 3 variations:</p> <ul> <li>``One obstacle" - features one obstacle in the environment and no robot</li> <li>``Moving robot" - features one obstacle in the environment and the moving robot</li> <li>``Three obstacles" - features three obstacles in the environment and no robot</li> </ul> <p><strong>THOR - people tracks </strong>is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation. The data are available in three formats:</p> <ol> <li>mat - Matlab binary file</li> <li>TSV - text file</li> <li>bag - ROS bag file</li> </ol> <p><strong>MAT files</strong></p> <ul> <li><strong>File </strong>- [char] Path to original QTM file</li> <li><strong>Timestamp </strong>- [string] Date and time of the startof the data collection</li> <li><strong>Start Fram </strong>- [char] 1</li> <li><strong>Frames </strong>- [double] Number of frames in the file</li> <li><strong>FrameRate</strong> - [double] Number of frames per second</li> <li><strong>Events</strong> - [struct] 0</li> <li><strong>Trajectories </strong>- [struct] 3D postion of observed reflective markers <ul> <li><strong>Labeled </strong>- [struct] Markers belonging to the tracked agents: <ul> <li><strong>Count </strong>- [double] Number of tracked markers</li> <li><strong>Labels </strong>- [cell] List of marker labels</li> <li><strong>Data </strong>- [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue</li> </ul> </li> </ul> </li> <li><strong>RigidBodies </strong>- [struct] 6D pose of the helmet, corresponds to head poistion and orientation: <ul> <li><strong>Bodies </strong>- [double] Number of tracked bodies</li> <li><strong>Name </strong>- [cell] Bodies Names</li> <li><strong>Positions </strong>- [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body</li> <li><strong>Rotations </strong>- [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body</li> <li><strong>RPYs </strong>- [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles</li> <li><strong>Residual </strong>- [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body</li> </ul> </li> </ul> <p><strong>TSV files</strong></p> <ol> <li><strong>3D data</strong> <ol> <li><strong>File Header</strong> <ul> <li>NO_OF_FRAMES - number of frames in the file </li> <li>NO_OF_CAMERAS - number of cameras tracking makers</li> <li>NO_OF_MARKERS - number of tracked markers</li> <li>FREQUENCY - tracking frequency [Hz] </li> <li>NO_OF_ANALOG - number of analog inputs </li> <li>ANALOG_FREQUENCY - frequency of analog input </li> <li>DESCRIPTION - --</li> <li>TIME_STAMP - the beginning of the data recording</li> <li>DATA_INCLUDED - the type of data included</li> <li>MARKER_NAMES - names of tracked makers</li> </ul> </li> <li><strong>Column names</strong> <ul> <li>Frame - frame ID</li> <li>Time - frame timestamp</li> <li>[marker name] [C] - coordinate of a [marker name] along [C] axis</li> </ul> </li> </ol> </li> <li><strong>6D data</strong> <ol> <li><strong>File Header</strong> <ul> <li>NO_OF_FRAMES - number of frames in the file </li> <li>NO_OF_CAMERAS - number of cameras tracking makers</li> <li>NO_OF_MARKERS - number of tracked markers</li> <li>FREQUENCY - tracking frequency [Hz] </li> <li>NO_OF_ANALOG - number of analog inputs </li> <li>ANALOG_FREQUENCY - frequency of analog input </li> <li>DESCRIPTION - --</li> <li>TIME_STAMP - the beginning of the data recording</li> <li>DATA_INCLUDED - the type of data included</li> <li>BODY_NAMES - names of tracked rigid bodies</li> </ul> </li> <li><strong>Colum Names</strong> <ul> <li>Frame - frame ID</li> <li>Time - frame timestamp</li> <li>The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix</li> </ul> </li> </ol> </li> </ol> <p><strong>Reference:</strong></p> <p>For more details check project website <a href="http://thor.oru.se">thor.oru.se</a> or check our publications:</p> <pre><code>@article{thorDataset2019, title={TH\"OR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset}, author={Andrey Rudenko and Tomasz P. Kucner and Chittaranjan S. Swaminathan and Ravi T. Chadalavada and Kai O. Arras and Achim J. Lilienthal}, journal={arXiv preprint arXiv:1909.04403}, year={2019} }</code></pre> <p> </p>
Mean radiant temperature along a common route for people experiencing homelessness in downtown Phoenix, Arizona (USA) on August 20, 2024
This tabular dataset contains mean radiant temperature (Tmrt) measurements collected using MaRTy, a mobile biometeorological, along a route frequently traveled by people experiencing homelessness in downtown Phoenix, Arizona (USA). It includes Tmrt, air temperature (Tair), relative humidity (RH), wind speed, and wind direction at pedestrian height at 2-second intervals for a typical summer day (August 20, 2024; peak air temperature of 43.3 degrees Celsius) at 0700, 1300, and 1700 (local times). This dataset can inform heat mitigation strategies for vulnerable populations in Phoenix.
Survey data on people's forest use patterns and perceptions of border security measures in Białowieża Forest region, Poland
<p>A survey was conducted in June-July 2022 to obtain information about people's forest use patterns and opinions and feelings about border security measures (state of emergency, border zone closure, militarization) instituted in northeastern Poland starting in September 2021. Participants were informed that the survey was voluntary and anonymous. Participants were not obliged to respond to all questions and could stop the survey at any time. Survey completion and submission implied consent to participate. Participants had to be at least 18 years of age to take part in the survey. They had to be residents of the Białowieża Forest region. 100 persons participated in the survey. Of these 100 persons, 44 identified as local (born in the region). Data are coded and a key is provided. Some responses are aggregated and only responses to close-ended questions are shared, to prevent disclosure of potentially identifying information. </p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
Implementation of Frailty Care Bundle (FCB) for older people in acute care settings
<p>A study aimed to implement a Frailty Care Bundle (FCB) for orthopaedic trauma patients to increase mobilisation, nutrition and cognitive well-being in order to reduce hospital associated decline risk.</p>
Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"
<p>Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"</p><p>====================================================</p><p>Datasets include: </p><p>1. .csv file with puma occurrence data in 149 sites and analyzed covariates (pumadata.csv)</p><p>2. .txt file with puma occupancy modelling scripts (pumascript.txt).</p><p>3. .txt. file with interview data (intdataf.txt).</p><p>4. .txt file with scripts for conflict risk modelling and mapping (confscript.txt).</p><p>5. three zip files with .tiff of the three covariates used for conflict risk mapping (goat.zip, disturbedpr.zip,</p><p>pue1km.zip).</p><p>6. .csv file with protected area data: number of pixels for each landscape type within all protected areas,</p><p>strict protected areas and multiple-use protected areas (PAchaco.csv).<br>7. Read me file with more information on datasets (README.txt)</p><p> </p>
Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A double-blind, randomized, crossover study.
<p><strong><span>Abstract: <span>Background/Objectives:</span></span></strong><span> Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson’s disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD. <strong>Methods:</strong> This randomized, double-blind, crossover trial included PwPD, who received transcutaneous KHFAC and sham interventions, within a 48h washout period. Measurements were taken pre-intervention, during, immediately after, and 10 minutes post-intervention. The main outcomes were rest, postural, and kinetic tremor acceleration. Secondary outcomes were handgrip strength, nine-hole peg test (NHPT), movement onset time, and adverse events.<strong> </strong></span></p>
Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)
<p>The dataset was created for the project "Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)". The data was collected between July 12 and July 26, 2024, from 1,030 young Czech people aged 16-25. The survey asked young people about their sociodemographic information, attitudes toward and perceptions of entitativity of three groups (overweight people, underweight people, people with physical disabilities), group identification, bystander appraisals and behavioural intentions, hate speech perception, and internet use. It included an experimental part in which the participants were exposed as bystanders to social media news posts about overweight people and comments under the posts. The dataset is accompanied by a data dictionary and a technical report.</p>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Data for 'Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro'
<p>Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’</p> <p>Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature.</p> <p>Principal Investigator:<br> - Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> * joint first-author<br> - Catherine A. Masao (ndeutz@yahoo.com, ORCID: 0000-0002-1242-9117) *<br> - Graham W. Prescott (graham.prescott.research@gmail.com, ORCID: 0000-0001-5123-514X) *<br> - Mark A. Snethlage (mark.snethlage@ips.unibe.ch, ORCID: 0000-0002-1398-8869) *<br> - Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) *<br> - Amor Torre-Marin Rando (amor.torre@ips.unibe.ch)<br> - Rafael Molina Venegas (rafmolven@gmail.com, ORCID 0000-0001-5801-0736)<br> - Neduvoto P. Mollel (neduvotomollel@yahoo.com, ORCID: 0000-0002-4402-4667)<br> - Claudia Hemp (claudiahemp@yahoo.com, ORCID: 0000-0002-5369-2122)<br> - Andreas Hemp (andreas.hemp@uni-bayreuth.de, ORCID: 0000-0001-9170-7113)<br> - Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: 2018-09<br> Location of data collection: Moshi, Kilimanjaro Region, Tanzania<br> Date of final file release: 2022-01-13</p> <p>Data Overview:</p> <p>We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’).</p> <p>We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale.</p> <p>File overview:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them.</p> <p>3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.</p> <p><br> Code used for analysis:<br> R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper</p> <p>File details:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>143 observations of 73 variables</p> <p>Key Variables:<br> - Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Biome2<br> (categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)<br> - Habitat.area<br> (categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)<br> - Habitat.condition<br> (categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)<br> - Habitat.area.will<br> (categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)<br> - Habitat.condition.will<br> (categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)<br> Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned.</p> <p>Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. </p> <p>Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution.</p> <p>Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology.</p> <p>Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>144 observations of 38 variables</p> <p>Key variables:</p> <p>- Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Service.original (free text response to which ecosystem service the participant was filling out the form)<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)<br> - Biome<br> (categorical - which habitat provided the ecosystem service)<br> - Why.changed.provision<br> (free text response for why Provision changed)<br> - Why.changed.access<br> (free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]<br> - Access<br> (categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)<br> - Access.will<br> (categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’)) </p> <p><br> 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>144 observations of 7 variables</p> <p>We took the following variables from the ecosystem services questionnaire:<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response)<br> - Access<br> (whether access to this NCP increased or decreased between 2008-2018)<br> - Why.changed.access<br> (free text response for why Access changed)<br> And created a new variable to synthesise the drivers of change in NCP access:<br> - Why.changed.access.code</p> <p>Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>11 observations of 6 variables<br> <br> We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.</p> <p> </p> <p> </p>
THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS
<p><strong>Dataset name:</strong><em> asppl_dataset_v2.csv </em></p> <p><strong>Version: </strong>2.0 </p> <p><strong>Dataset period: </strong>06/07/2018 - 01/14/2022</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>8118</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c); </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>asppl_dataset_v2.csv</em> dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)</p> </td> </tr> <tr> <td> <p><strong>course_progress</strong></p> </td> <td> <p>Percentage of completion of the course. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>Range from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). </p> </td> </tr> <tr> <td> <p><strong>CNES</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the participant works.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>CNES Code or NaN.</p> </td> </tr> <tr> <td> <p><strong>health_care_level</strong></p> </td> <td> <p>Identification of the health care network level for which the course participant works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.<br> <br> (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE") </p> </td> </tr> <tr> <td> <p><strong>year_enrollment</strong></p> </td> <td> <p>Year in which the course participant registered.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Year (YYYY).</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>prison_syphilis_and_population_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>13</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>National Penitentiary Department (DEPEN) (Brasil, 2022d); </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>prison_syphilis_and_population_brazil.csv</em> dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.</p> <p><strong>Table 2:</strong> Description of DEPEN dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2017</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2017</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2018</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2018</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2019</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2019</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2020</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2020</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>pop_2017</strong></p> </td> <td> <p>Prison population in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2018</strong></p> </td> <td> <p>Prison population in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2019</strong></p> </td> <td> <p>Prison population in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2020</strong></p> </td> <td> <p>Prison population in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>students_cumulative_sum.csv</em></p> <p><strong>Dataset period: </strong>2018 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: 7</strong></p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>students_cumulative_sum.csv</em> dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 3:</strong> Description of Students dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>Number of students enrolled in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Standardized rate of students in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Standardized rate of students in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Standardized rate of students in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>syphilis_tests_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>syphilis_tests_brazil.csv</em> dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 4:</strong> Description of Syphilis Testes dataset Features. </p> <table align="center"> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p>Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>2017</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2017</strong></p> </td> <td> <p>Syphilis testing rate in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Syphilis testing rate in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Syphilis testing rate in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Syphilis testing rate in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> .</p> <p>Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> .</p> <p>Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> .</p> <p>Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> .</p> <p>Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> .</p> <p>Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> .</p> <p>Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS <br> </p> <p><strong>AUTHORS:</strong></p> <p>Janaína L. R. S. Valentim<sup>1,2</sup>, Sara Dias-Trindade<sup>2,3</sup>, Eloiza da S. G. Oliveira<sup>1,4</sup>, José A. M. Moreira<sup>2,5</sup>, Felipe Fernandes<sup>1</sup>, Manoel Honório Romão<sup>1</sup>, Philippi S. G. de Morais<sup>1</sup>, Alexandre R. Caitano<sup>1</sup>, Aline P. Dias<sup>1</sup>, Carlos A. P. Oliveira<sup>1,4,6</sup>, Karilany D. Coutinho<sup>1</sup>, Ricardo B. Ceccim<sup>7</sup>, Ricardo A. M. Valentim<sup>1</sup></p> <p> </p> <p><sup>1</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>2</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal</p> <p><sup>3</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal</p> <p><sup>4</sup>Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil</p> <p><sup>5</sup>Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal</p> <p><sup>6</sup>International Council for Open and Distance Education, Oslo, Norway</p> <p><sup>7</sup>Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil</p> <p> </p> <p> </p>
Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease
<p>Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.</p>
Young people's media use and adherence to preventive measures in the "infodemic": Is it masked by political ideology?
<p>Data to replicate the publication "Young people's media use and adherence to preventive measures in the “infodemic”: Is it masked by political ideology?". This publication examines the role of political ideology and political extremism for COVID-19 information seeking and preventive behaviour with data of the COVIDisc project. COVIDisc investigates how young people aged 15 to 34 years perceive the discussion in the Coronavirus Pandemic, which messages reach them, what media they use to inform themselves and how they experience the situation. en</p>
Self-reported data for Sustainable Development from people living in rural and remote areas
<p>Anonymous self-reported data from people living in rural/remote areas as part of a research project on Sustainable Development.</p> <p>The data collection has been approved by the University of Technology Sydney (UTS HREC REF NO. ETH24-9191).</p> <p>The version 1.0 of the dataset includes 212 valid answers to 40 core questions (+ additional info) collected in 2024 in Saudi Arabia.</p>
CLDF dataset derived from Tolmie and Dawson's "Comparative Vocabulary of the Indigenous Peoples in British Columbia" from 1884
<p>Cite the source of the dataset as:</p> <blockquote> <p>Tolmie, Fraser W. and Dawson, George M. (1884). Comparative vocabularies of the Indian tribes of British Columbia, with a map illustrating distribution. Montreal: Dawson Brothers.</p> </blockquote>
Data on eye movements in people with glaucoma and peers with normal vision
<p>Eye movements were recorded from 44 elderly glaucoma patients and 32 age-similar healthy vision controls whilst watching three separate small video clips.</p>
3D printed map for blind or visually impaired people
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used to apply the methods of relief editing and image processing to facilitate the production of accessible documentation by having in hand an easy to use interface.</p>
WYRED Platform, the ecosystem for the young people
<p>The WYRED Platform is a technological ecosystem developed as part of WYRED (netWorked Youth Research for Empowerment in the Digital society), a European Project funded by the Horizon2020 programme.</p> <p>As society changes, there is a need to understand how it is changing, to explore what is going on. The young people have a key role to play in our society. They are frequently the drivers of new behaviours and understandings, and since they are part of the future society their views and perceptions should be considered. However, they are not well represented and their voices are unheard, and this makes it hard for research and policy to identify and understand their needs.</p> <p>The aim of the WYRED Platform is to provide the tools to support dialogue and research processes in which children and young people can express and explore the key issues that they consider as important.</p> <p>To design it, different stakeholders were involved, and several questionnaires and social dialogues were carried out.</p> <p>The WYRED Platform is organized in multicultural and interdisciplinary communities where young people can develop research projects with the support of facilitators from different European institutions and associations. The communities have different tools such as forums to establish dialogues and coordinate research cycles, calendars to share dates and organize events or activities, surveys to develop or evaluate the projects, a version control system for files to support the documentation generated during the research processes. Also, it provides a tool to publish the results of the research projects.</p> <p>One of its main innovations is the strongly commitment to user privacy, it is designed as a safe space in which children and young people can be free to express themselves as they wish. Moreover, it design is centred around and driven by children and young people.</p> <p> </p> <p><strong>Video link</strong>: <a href="https://youtu.be/TRDjN5boky8">https://youtu.be/TRDjN5boky8</a> (presented in the student design competition: video presentations of the HCI International 2018, held in Las Vegas, NV, USA, July 15-20, 2018).</p>
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.