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123 results for “Tourism”
Sustainable Tourism Survey Dataset for the Northern Ecuadorian Amazon Region, 2024-2025
This dataset is based on a sustainability perception survey conducted at Perla Ecological Park, located in the Northern Ecuadorian Amazon, during December 2024 and January 2025. A total of 383 visitors participated in the study, which aimed to: (1) quantify and compare key sustainability indicators across PERLA’s management zones, (2) identify the most influential predictors of overall sustainable performance, and (3) derive and prioritize a set of integrated strategies that balance ecological conservation with economic viability. The survey instrument included Likert-scale, dichotomous, and thematic categorical items designed to assess public perceptions on tourism sustainability, natural and cultural resource management, institutional support, and visitor satisfaction. The research was carried out through a collaborative effort among multiple public universities and independent researchers, including two international institutions—one of them based in Ecuador—as part of a broader scientific initiative to inform evidence-based sustainability planning in protected areas. This dataset provides valuable insight for researchers, practitioners, and policymakers interested in sustainable tourism, visitor management, and participatory planning in biodiversity-rich environments.
Systematic Literature Review on Tourism Marketing in the Metaverse
<p>The aim of this research is to investigate tourist marketing within the embryonic context of the metaverse in order to comprehend the building blocks and the primary technologies employed in the sector. For this purpose, a systematic literature review is conducted. The references are extracted in January 2023. The data in this document correspond to the articles finally included in the systematic literature review after the article screening phase.</p> <p>Keywords: tourism marketing, metaverse, technologies, building blocks, SLR (Systematic Literature Review), PRISMA</p> <p> </p>
Data on Memorable Tourism Experiences
<p>This is a data collected from Indian and International tourist who have travelled for leisure. The focus is on memorable tourism experiences. It has also captured the visitors intention to revisit or extend their stay. It was collected from December 2018 - February 2020. I want research scholars, students, and academicians to make best use of this data and share any kind of analysis or papers published with me on chervendias@gmail.com.</p>
Desk research of 107 case studies on state-of-the-art of cultural tourism interventions
<p>The aim of Work Package 3 of the SmartCulTour project (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) is to provide a state-of-the-art overview of cultural tourism interventions implemented in European cities and regions, thereby identifying best practices and the impacts and success conditions of cultural tourism interventions. To this end, the consortium identified 107 interesting cultural tourism interventions throughout Europe, with a <strong>geographical coverage</strong> of: Belgium (9), Italy (8), The Netherlands (8), Serbia (7), Romania (6), Croatia (5), Hungary (4), Portugal (4), Spain (4), United Kingdom (4), Finland (3), France (3), Sweden (3), Other countries (24), Multiple countries (15). The "Overview and taxonomy of 107 interventions" lists every intervention that was studied, as well as their respective classification given, based on the description and objective of the intervention. Within this table, a value of "1" is its primary categorization, with a value of "2" assigned to a secondary taxonomy. Each intervention can have multiple purposes and therefore belong to different categories.The taxonomy of cultural tourism interventions is further described in "State of the art of cultural tourism interventions" (DOI: 10.5281/zenodo.5270321).</p> <p>The 107 cultural tourism interventions were analyzed via desk research only during the period September 2020-January 2021, based on available secondary data and following a standardized <strong>data collection form</strong>. This form is included here as "Internal data collection form used for analysis". The forms collect data on:</p> <ul> <li>Context and background information;</li> <li>The 'reason why' of the intervention;</li> <li>The intervention;</li> <li>Resources and tools necessary to design and implement the interventions;</li> <li>Impacts (expected, perceived and measured);</li> <li>(Perceived) success conditions and limiting factors.</li> </ul> <p>The zip-file "Internal forms of 107 interventions" contains all 107 completed data collection forms.</p> <p> </p>
Engaging the Business and Tourism Industry in Visualizing Sea Level Rise Impacts to Transportation Infrastructure in Waikiki, Hawaii
<p>Transportation planners in coastal communities plan for future hazards and risks of sea-level rise (SLR), and often, they communicate risk in public meetings via PowerPoint presentations with charts as well as two-dimensional (2D) maps that visualize information using Geographic Information Systems (GIS) technologies. The project investigates the use of immersive technology to communicate SLR risk, including the development of an immersive three-dimensional (3D) model of the Waikiki neighborhood of Honolulu, Hawaii. According to the project’s original methodology, participants would have experienced the model using virtual reality (VR). However, due to the COVID-19 pandemic, the team pivoted to creating and implementing an internet-based survey instrument with embedded 2D charts and video of the animated 3D model. The flooding projections were derived from National Oceanic and Atmospheric Administration (NOAA) data. NOAA supplies the SLR Viewer, a screening-level tool that uses the best-available national projections to map areas vulnerable to current and future flood risks.</p>
Dataset about engagement and tourism variables
<p>This file contains engagement data for all posts published by info.Spain in 2017. All information collected to create this dataset is public domain. This dataset provides information on posts that pursue the positioning of the Spanish national brand, as well as indicators of tourist engagement with these posts, and indicators related to tourism variables. </p>
Tourism area dataset in Japan
<p>Tourism areas and spots</p> <h3>Tourism area data</h3> <p><code>areas.csv</code><br>Tourism areas in Japan derived from Wikipedia [1]. It includes 378 areas. Each line has the following format as tab delimited: <br><code>area_id, area_name</code><br>Here, the area_id denotes a tourism area ID. The area_name denotes a tourism area name in Japanese.</p> <p><code>areas_admin_areas.csv</code><br>Relation between tourism areas and administrative areas. Each line has the following format as tab delimited: <br><code>area_id, administrative_area_code</code><br>Here, the area_id refers to the area_id in the file areas.csv. The administrative_area_code refers to the administrative area code in the administrative area data [2] provided by the National Land Numerical Information data.</p> <h3>Category data</h3> <p><code>categories.csv</code><br>Categories of tourism spots. It includes 58 categories. Each line has the following format as tab delimited: <br><code>category_id, category_1, category_2</code><br>Here, the category_id denotes a category ID. The category_1 denotes the category name and the category_2 denotes the sub category name in Japanese.</p> <h3>Tourism spot data</h3> <p><code>spots.csv</code><br>Tourism spots in Japan derived from Wikipedia [1]. It includes 12,659 spots. Each line has the following format as tab delimited: <br><code>spot_id, spot_name</code><br>Here, the spot_id denotes a tourism spot ID, which corresponds to the page ID in the Wikipedia [1]. The spot_name denotes a tourism spot name in Japanese, which corresponds to the page title in the Wikipedia.</p> <p><code>spots_latlng.csv</code><br>Location of tourism spots. It includes 8,645 spots. Each line has the following format as tab delimited: <br><code>spot_id, lat, lng</code><br>Here, the spot_id refers to the spot_id in the file spots.csv. The lat and lng denote latitude and longitude of the spot, respectively.</p> <p><code>spots_categories.csv</code><br>Relation between tourism spots and categories. It includes 9,905 relations and 9,840 unique spots. Each line has the following format as tab delimited: <br><code>spot_id, category_id</code><br>Here, the spot_id refers to the spot_id in the file spots.csv. The category_id refers to the category_id in the file the categories.csv.</p> <p><code>spots_areas.csv</code><br>Relation between tourism spots and tourism areas. It includes 12,065 relations and 11,888 unique spots. Each line has the following format as tab delimited: <br><code>spot_id, area_id</code><br>Here, the spot_id refers to the spot_id in the file spots.csv. The area_id refers to the area_id in the file the areas.csv.</p> <h2>Tourism area vectors</h2> <h3>Land vectors data</h3> <p><code>areas_land_vec.csv</code><br>Land vectors of tourism areas derived from land use data [3] provided by the National Land Numerical Information data. It includes 378 areas. Each line has the following format as tab delimited: <br><code>area_id, 100, 200, 500, 600, 700, 901, 902, 1000, 1100, 1400, 1500, 1600</code><br>Here, the area_id refers to the area_id in the file the areas.csv. The last 12 fields denote land vector and each field denotes the land use type code [3] provided by the National Land Numerical Information data.</p> <p><code>areas_elevation_vec.csv</code><br>Elevation vectors of tourism areas derived from elevation and gradient data [4] provided by the National Land Numerical Information data. It includes 378 areas. Each line has the following format as tab delimited: <br><code>area_id, elev_0.0, elev_0.1, ..., elev_3.6</code><br>Here, the area_id refers to the area_id in the file the areas.csv. The last 37 fields denote elevation vector.</p> <p><code>areas_category_vec.csv</code><br>Category vectors of tourism areas. It includes 378 areas. Each line has the following format as tab delimited: <br><code>area_id, 1, 2, ... 58</code><br>Here, the area_id refers to the area_id in the file the areas.csv. The last 58 fields denote cateogory vector and each field refers to the category_id in the file categories.csv.</p> <h2>References</h2> <ol> <li><a title="Wikipedia" href="ja.wikipedia.org/wiki/" target="_blank" rel="noopener">Wikipedia</a>, in Japanese.</li> <li><a title="国土数値情報 | 行政区域データ" href="https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N03-v3_1.html" target="_blank" rel="noopener">Administrative area data</a>, National Land Numerical Information data, in Japanese.</li> <li><a title="国土数値情報 | 土地利用3次メッシュデータ" href="https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-L03-a-v3_1.html" target="_blank" rel="noopener">Land use data</a>, National Land Numerical Information data, in Japanese.</li> <li><a title="国土数値情報 | 土地利用3次メッシュデータ" href="nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-G04-a.html" target="_blank" rel="noopener">Elevation and gradient data</a>, National Land Numerical Information data, in Japanese.</li> </ol> <h2><strong>Citation</strong></h2> <p>To make use of the dataset, please cite one of the following papers:<strong><br></strong></p> <ul> <li>Kenta Oku. Construction of a tourism area dataset using open data. Society for Tourism Informatics, Vol.20, No.1, pp.45–64, 2024 (in Japanese).</li> <li>奥健太. オープンデータを用いた観光地エリアデータセットの構築. 観光と情報:観光情報学会誌, 第20巻, 第1号, pp.45-64, 2024.</li> </ul> <h2>Acknowledgments</h2> <p>This work was supported by JSPS KAKENHI Grant Number JP19K12567.</p>
Perception survey 2023-2024 Bibracte tourism observatory
<p>In 2018 and 2019, Bibracte set up a new survey system, EVALTO, which enabled to cross-reference data from questionnaires administered mainly face-to-face to <strong>visitors, elected representatives and socio-professionals, and local residents</strong>. The aim was to go further than traditional visitor surveys by, on the one hand, collecting data on visitor profiles and practices and, on the other, questioning local players about their perceptions of tourism, in order to share an objective view of tourist activity and thus move towards genuine territorial <em>tourism intelligence</em>. The system was reactivated in 2023 and the questionnaire has been completed as part of the INCULTUM project.</p> <p><u>Sets of data available :</u></p> <ul> <li><strong>Visitors to the Bibracte site: collected from April to September </strong><strong>2023</strong><br>Excel document attached extracted from the Sphinx software INCULTUM_BI_visitors-survey_2023._rawdata.csv</li> <li><strong>Hikers survey on the territory of Bibracte-Morvan des Sommets</strong><br>Data for the 2023 survey are in the Excel document extracted from the Sphinx software INCULTUM_BI_hikers_surevy_2023_rawdata.xls</li> </ul> <p><u>Set of data that will be available later in 2024:</u><br>- <strong>Inhabitants of the wider area of the Grand Site de</strong> France: 50 collected in 2023 out of 200 targeted, the rest will be carried out in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.<br>- <strong>socio-professionals and elected representatives</strong>: 34 responses in 2023 out of 200 targeted; the follow-up will be carried out in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.<br>- <strong>motor home owners</strong>: 93 responses in 2023 out of 200 targeted, with a follow-up in 2024. These questionnaires are in paper format and have not yet been entered into the Sphinx software.</p>
In-depth analysis of 18 case studies on state-of-the-art of cultural tourism interventions
<p>The aim of Work Package 3 of the SmartCulTour project (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) is to provide a state-of-the-art overview of cultural tourism interventions implemented in European cities and regions, thereby identifying best practices and the impacts and success conditions of cultural tourism interventions. Based on the previous, desk-researched 107 interesting cultural tourism interventions see "Desk research of 107 case studies on state-of-the-art of cultural tourism interventions", DOI: 10.5281/zenodo.5213017 ), a further 18 interventions were selected for in-depth analysis.</p> <p>These 18 cultural tourism interventions were analyzed via a series of expert interviews, combined with document and literature analysis, performed in the period January 2021-March 2021. Description of the case studies follows a standardized <strong>case study data collection form</strong>. This form is included here as "Case study data collection form". The form collect data on:</p> <ul> <li>Context and background information;</li> <li>The 'reason why' of the intervention;</li> <li>The intervention;</li> <li>Resources and tools necessary to design and implement the interventions;</li> <li>Impacts (expected, perceived and measured);</li> <li>(Perceived) success conditions and limiting factors.</li> </ul> <p>The semi-structured interviews left some room for adaptability depending on the case studies. A <strong>general guide on potential interview questions</strong> is included here as "Potential questions list for case study interviews".</p> <p>All case studies are included as individual odt-files, starting with "detailed case study + name of the intervention".</p>
Dataset: Cross-Sectional National Survey on Risk Perception and Tourism Behaviour (SNF NRP 78)
<p>The data set contains scales (<em>rating items</em>) on the willingness of the Swiss resident population to take risks in connection with touristic travel during the coronavirus pandemic. The data includes a selection of items of the <em>Domain-Specific Risk-Taking Scale</em> (<em>DOSPERT) </em>(Weber et al., 2002). The data contains measures of the <em>health belief model</em> (<em>HBM</em>; Rosenstock, 1960, see also Champion & Skinner, 2008) that is used both in health research and in tourism research to explain and predict the preventive health behaviour of individuals. Furthermore, the data covers all three elements of the t<em>heory of planned behaviour (</em>Ajzen, 1991). This is a representative data set for the Swiss population aged 18 and above. A trilingual and national survey of the Swiss resident population was carried out in the period from March to May 2021. A letter of invitation to participate in the study was sent by post to a total of 4,530 randomly selected persons residing in Switzerland. The address data was provided by the Federal Statistical Office (BfS). Of the total of 4,530 people contacted, 164 were reported as unreachable (no longer at the address, deceased, or due to old age). A total of 1,683 persons participated in the survey. This corresponds to a response rate of 39%. The structure of the respondents corresponds to that of the Swiss resident population 18 years of age and older with regard to gender, age, and language region.</p> <p>Ajzen, I. (1991). The theory of planned behavior. <em>Organizational Behavior and Human Decision Processes</em>, <em>50</em>(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T</p> <p>Weber, E., Blais, A.-R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. <em>Journal of Behavioral Decision Making</em>, <em>15</em>, 263–290. https://doi.org/10.1002/bdm.414</p> <p>Champion, V. L., & Skinner, C. S. (2008). The health belief model. In K. Glanz, B. Rimer, & K. Viswanath (Eds.), <em>Health behavior and health education: Theory, research, and practice</em> (4th ed., pp. 45–65). San Francisco, CA: Jossey-Bass.</p> <p>Rosenstock, I. M. (1960). What research in motivation suggests for public health. <em>American Journal of Public Health, 50</em>(3), 295-302. https://doi.org/10.2105/AJPH.50.3_Pt_1.295</p>
Hospitality and Tourism Industry from TR-HT
<p>Set of data used in the paper <em>The impact of ESG dimensions on firm risk of hospitality and tourism industry.</em></p> <p>The data were obtained from Thomson Reuters Eikon database (TR_Eikon), we selected those companies whose activity sector was H&T regardless of their country of origin. The data are due in "xls" format and structured in two sheets.</p> <p>- the first sheet contains the selected companies, with the next information: </p> <ol> <li>Company Name</li> <li>NAICS Sector Code</li> <li>NAICS Subsector Code</li> <li>NAICS Industry Code</li> <li>NAICS National Industry Name</li> <li>Muestra Final: an indicator variable used to remark that the respective company is used in the study.</li> </ol> <p>- The second sheet contains the next information:</p> <ol> <li>Company Id: The Company Identificator. A number differentiating each company from the rest.</li> <li>Year: The year of the correspondig register.</li> <li>P_Crisis: Pandemic Crisis. Dummy variable coded 1 if year is 2020, 0 otherwise.</li> <li>F_Crisis Financial Crisis: Dummy variable coded 1 if year is from 2008 to 2012, 0 otherwise</li> <li>Firm_Size: Firm size. Logarithm of total asses</li> <li>Leverage: Leverage. Total debt to total assets</li> <li>ROA: Return on assets. EBITDA divided by total assets</li> <li>B_Gender: Gender diversity. Number of women directors as a percentage of total directors on the board</li> <li>B_Independence: Independence. number of independent directors as a percentage of total directors on the board</li> <li>B_Size: Board size. Number of directors on the board</li> <li>Duality: CEO duality. Dummy variable taking the value 1 if the chairperson of the board is the CEO and 0 otherwise</li> <li>ESG_Score: Evironmental Social and Governance Score. A weighted average relative rating based on reported environmental, social and governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>SOC_Score: Social Pillar Score. A weighted average relative rating based on reported social information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>GOV_Score: Governance Pillar Score. A weighted average relative rating based on reported governance information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>ENV_Score: Environmental Pillar Score. A weighted average relative rating based on reported environmental information and ranges between 0 (worst) and 100 (best). It is provided by Eikon Thomson Reuters</li> <li>Dependent variables</li> <li>D2D: Distance of Default. Merton’s distance to default</li> <li>SD_R: Volatility of the stock returns. Standard deviation of daily stock returns</li> </ol>
Biodiversity and infrastructure interact to drive tourism to and within Costa Rica
Significance Tourism accounts for roughly 10% of global gross domestic product, with nature-based tourism its fastest-growing sector in the past 10 years. Nature-based tourism can theoretically contribute to local and sustainable development by creating attractive livelihoods that support biodiversity conservation, but whether tourists prefer to visit more biodiverse destinations is poorly understood. We examine this question in Costa Rica and find that more biodiverse places tend indeed to attract more tourists, especially where there is infrastructure that makes these places more accessible. Safeguarding terrestrial biodiversity is critical to preserving the substantial economic benefits that countries derive from tourism. Investments in both biodiversity conservation and infrastructure are needed to allow biodiverse countries to rely on tourism for their sustainable development.
The Dataset of Smart Tourism Technologies in Indonesia - 397 Records - 2024
<p>The rapid evolution of Information and Communication Technologies (ICT) has significantly advanced smart tourism technologies (STTs), enhancing tourists' experiences by providing seamless access to destination information. Despite these advancements, the attributes of STTs in Indonesia still require considerable improvement to better support and assist tourists. A comprehensive 2024 dataset has been developed to examine various factors influencing the effectiveness of STTs in Indonesia. Key variables include Accessibility, Informativeness, Interactivity, Personalization, Security, Memorable Experience, and Satisfaction. These variables have been found to have a substantial impact on the overall utility and effectiveness of STTs. Accessibility refers to the ease with which tourists can use the technologies, while Informativeness pertains to the quality and depth of information provided. Interactivity involves the degree to which tourists can engage with the technology, enhancing their overall experience. Personalization focuses on the ability of the technology to cater to individual preferences, making the experience more relevant and enjoyable for each tourist. Security is crucial in ensuring tourists feel safe and protected while using these technologies. This dataset provides valuable insights into the various attributes of STTs in Indonesia, highlighting areas for improvement to better meet the needs of tourists and enhance their overall experience. The dataset consists of 397 records from respondent surveys, which are substantial for data analysis.</p>
Figure 2. – Mean abundance per 750 m2 in Changes in distribution patterns of two vulnerable fish species (Epinephelus marginatus and Sciaena umbra) in the Scandola marine reserve (Corsica, NW Mediterranean): a possible effect of increased boat tourism
Figure 2. – Mean abundance per 750 m2 (± SE) of the dusky grouper Epinephelus marginatus (A) and the brown meagre Sciaena umbra (B) according to protection level at Scandola in 2012 and 2018. IR: integral reserve, BZ: buffer zone, UP: unprotected zone. Interannual difference are indicated for each protection level, *: significant at p <0.05, ns: not significant.
Figure 1 in Changes in distribution patterns of two vulnerable fish species (Epinephelus marginatus and Sciaena umbra) in the Scandola marine reserve (Corsica, NW Mediterranean): a possible effect of increased boat tourism
Figure 1. – Location of sites surveyed for dusky grouper and brown meagre populations inside and outside the Scandola MNR (Corsica, NW Mediterranean), according to protection status in summer 2012 and 2018. Dark blue arrows = integral reserve (IR), light blue arrows = buffer zone (BZ), red arrows = unprotected zones (UP). Blue stars = surveyed only in 2012, red star = site surveyed only in 2018.
Figure 5 in Changes in distribution patterns of two vulnerable fish species (Epinephelus marginatus and Sciaena umbra) in the Scandola marine reserve (Corsica, NW Mediterranean): a possible effect of increased boat tourism
Figure 5. – Size structure of Epinephelus marginatus and Sciaena umbra at Scandola in 2012 and 2018. Size classes = 10 cm for the dusky grouper and 5 cm for the brown meagre.
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 1. Selected Romanian counties in order to strengthen the target group of tourism organizations with informative role
<p>In this regard, we analysed the current state of presence and communication on Facebook for<br> 109 informative tourism entities located in 25 Romanian counties, selected on the basis of tourist<br> traffic indicators for the period between 2007 and 2013. The structure of the 109 organizations<br> analysed is: 43 tourist information centers (39.45%), 44 entities with the name of the association for<br> tourism promotion, ecotourism promotion, mountaineering promotion etc. (40.36%), 18 tourism<br> clubs (16.51%) and 4 tourist information points/offices (3.67%)</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 3. Integration of Social Media elements on tourism organization's websites
<p>Currently, in Romania there are about 8 million Facebook users (Facebrands.ro). In the recent years there has been a spectacular increase of this phenomenon, which shows how important is the use of social networks for an economic and even for a non-profit entity in order to make the brand known or to promote an activity (DailyBusiness.ro). </p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 2. Online presence through a website of Romanian tourism entities with informative role Source: authors
<p>According to research results (Figure 2), almost 68% of the entities with tourist information and promotion role own a proper site for the presentation of the work, while 18.35%, most probably do not realize in pragmatic terms the usefulness of such promotional tools. The situation can be cataloged as quite worrying, especially if we consider that today, due to the fulminant development of smartphones, more and more tourists choose to seek information on the Internet, even during their trip to a new destination (Wang el. al. 2012).</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 5. The attendance rate and active communication on Facebook of the Romanian tourism organizations with informative role
<p>As we can see in Figure 5, the presence on Facebook of the organizations involved in information and promotion of tourism activities (54.12%) is lower than the rate of online presence through a website (67.89%), which broadly confirms that Social Media visibility is the second step in the strategy of online business promotion of entities that were subject of this research. The fact that 99% of the tourist organizations present on Facebook already have a website that promotes their own work confirms the previous statement</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.