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388 results for “Intentions”
Moral judgments of intentional and accidental moral violations across Harm and Purity domains
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Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
Survey data on financial literacy, financial inclusion, informal financial business practices, and intentions towards formalization of female small vendors in Lima, Peru
<p><span>This dataset encapsulates a comprehensive survey aimed at understanding informal business practices and financial literacy among small business vendors in Peru. The dataset comprises three key components: the survey questionnaire, raw survey data, and a detailed codebook. Researchers interested in the dynamics of financial practices in emerging markets may find this dataset particularly valuable, as it allows for the exploration of factors influencing financial decisions in small enterprises, with potential modifications suggested for adapting the survey to different national or cultural contexts. This dataset not only contributes to empirical research in financial behavior but also supports gender-specific studies by allowing the variable 'sex' to be adapted to 'gender' with multiple response options. </span></p> <p><span>The data and supplementary material is divided in tree files:</span></p> <p><span>The survey, presented in "Survey IFE.docx," includes questions across various domains such as informal business practices, financial literacy, financial inclusion, intentions towards financial formalization, and the formality of business ventures, along with demographic variables like age, sex, business age, and number of employees. </span></p> <p><span>The raw data, stored in "Dataset.csv," records responses from 118 participants, mapped against 31 indicators. </span></p> <p><span>The "Codebook.doc" provides exhaustive details about the survey variables, coding of responses, and the methodology employed, facilitating the replication of the study and application of the dataset in varied research contexts.</span></p>
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
Intention de parler positivement des LGBT chez les jeunes en France. Une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 372, année : 2020). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’intention Intention de parler positivement des LGBT lors d'une discussion. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 1 item de connaissance objective, 1 item d’engagement dans le questionnaire, 7 variables de segmentation, l’intention comportementale (2 items dont 1 d’identité personnelle ; cf. infra pour la justification théorique), les croyances sur les bénéfices attendus (9 items), l’attitude (3 items), les croyances sur les freins perçus (7 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items) et 5 variables de signalétique. L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 7 points.</p>
Intention de soutenir la légalisation du cannabis chez les jeunes en France : une application de la théorie du comportement planifié.
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 434, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’intention de soutenir la légalisation du cannabis lors d'une discussion. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 4 variables de signalétique, l’intention comportementale (2 items dont 1 d’identité personnelle), les croyances sur les bénéfices attendus (14 items), l’attitude (3 items), les croyances sur les freins perçus (7 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), les comportements de consommation de cannabis et les modalités de la légalisation (7 items). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Covid-19 et Intention d'utiliser les chèques psycho par les étudiants : la prise en compte du contexte dans la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (nombre d’observations = 460, période : mars 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable finale que le modèle cherche à expliquer (la variable dépendante) est l’intention d’utiliser les chèques psycho, dispositif proposé par le gouvernement français en février 2021, par les étudiants. Ces chèques permettent aux étudiants de bénéficier de 3 consultations auprès d’un psychologue conventionné, pour un montant total de 96 €.</p> <p>Les objectifs (et les utilisations possibles) de cette BDD sont autant orientés vers les besoins des acteurs (notamment ceux en charge de la mise en œuvre du dispositif « chèque psycho » pour les étudiants ou de dispositifs similaires) que des chercheurs.</p> <p>L’objectif opérationnel est de mesurer (statistiques descriptives) et de comprendre (statistiques explicatives) les facteurs qui conduisent des étudiants à envisager d’utiliser ce dispositif ; et donc à pouvoir comprendre comment agir pour optimiser cette utilisation. L’extension récente (juin 2021) de ce dispositif aux enfants et adolescents de 3 à 17 ans (dispositif « PsyEnfantAdo ») induit un deuxième objectif opérationnel : la possibilité pour les acteurs en charge de ce nouveau dispositif de s’inspirer de la méthodologie présentée dans cette base pour piloter au mieux ce projet.</p> <p>L’objectif théorique réside a) dans la prise en compte de l’impact d’un contexte (le vécu des étudiants durant la pandémie de la Covid-19 (t leur antécédents au niveau psychologique dans la théorie du comportement planifié (TCP) et b) dans la confirmation de la place de l’identité personnelle en tant que mesure alternative de l’intention comportementale (et non en tant que variable explicative de cette intention). Les chercheurs pourront également utiliser cette base dans des méta-analyses sur la TCP, la prise en compte du contexte dans la compréhension de l’intention comportementale et les impacts de la Covid-19.</p>
The Robot Joint Torque Measurements for Accidental Collisions and Intentional Contacts
<p>This dataset contains the joint toque measurements of a robot manipulator (<a href="https://blog.robotiq.com/bid/64944/Collaborative-Robot-Series-KUKA-s-Light-Weight-Robot-4">KUKA LWR4+</a>) under accidental collisions and intentional contacts. It is specifically intended for the research study on robot collision detection, classification, diagnosis, or prediction. The dataset was recorded at <a href="https://www.ce.cit.tum.de/en/lsr/home/">Chair of Automatic Control Engineering</a>, <a href="https://www.tum.de/en/">Technical University of Munich</a>, Munich, Germany, by <a href="https://sites.google.com/view/zengjie-zhang/home">Dr. Zengjie Zhang</a>, under the supervision of <a href="https://www.ce.cit.tum.de/lsr/team/dozenten/dirk-wollherr/">Dr. Dirk Wollherr</a>, in 2017. Its detailed recording procedure is explained in the following work:</p> <p>[1] <strong>Zhang Z</strong>, Qian K, Schuller B W, and Wollherr D. An online robot collision detection and identification scheme by supervised learning and bayesian decision theory[J]. <em>IEEE Transactions on Automation Science and Engineering</em>, 2020, 18(3): 1144-1156.</p> <p>The dataset contains a number of external signal pieces of three classes: accidental collision (cls), with intentional manual contacts (ctc), and free from contacts (fre). Each signal piece lasts for 1.024s subject to the sampling rate 1kHz. Collisions or contacts occur at 0.256s of the signal pieces. The unit of the signal measurement is Nm. All the signals are recorded for the seven joints (#1 to #7) of the KUKA robot arm.</p> <p>The dataset is stored in .csv files. Each .csv file, containing the torque signal pieces for each class and each joint, is formed as an N by M matrix, where M = 1024 is the length of the signals and N is the number of signal pieces of the corresponding classes. For 'cls', N = 6960; for 'ctc', N = 7583; and for 'fre', N = 14098. Refer to the 'ReadMe.md' file for how to import the data to Python or MATLAB.</p> <p>This dataset is openly accessible for research work. Please cite this dataset and reference [1] if you publish the work based on them.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
An exploratory study of consumers' familiarity, attitude, and purchase intentions for lignin-based sunscreens and bio-based skincare products [dataset]
<p>Dataset for <em>An exploratory study of consumers’ familiarity, attitude, and purchase intentions for lignin-based sunscreens and bio-based skincare products.</em></p>
Risk-perception, attitudes and behavioural intentions to spend on experiences in the post-Corona crisis: data from Italy, Denmark, China and Japan
<p>A cross-sectional survey conducted in Japan (n=1,111), Denmark (n=1,028), China (n=1,019) and Italy (n=1,014) during 10-24th of July 2020.</p> <p>Data format: sav (SPSS) and csv.</p>
Wikidata CiTO intention annotations
<p>Dump using three SPARQL queries without provenance of the CiTO intention annotations collected in Wikidata.</p>
Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention
<p>This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication:</p> <ul> <li><em>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck</em>, <strong>Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments</strong>, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) <em>(under review)</em></li> </ul> <p>A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper.</p> <p>The file names are structured as follows:</p> <ul> <li><em>1st/2nd:</em> <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li><em>unfiltered:</em> Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li><em>train/val/test:</em> The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> <p>Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present.</p> <ul> <li>General: <ul> <li><em>time:</em> in seconds</li> <li><em>subject:</em> consecutive subject number</li> <li><em>handedness:</em> left (1), right (0)</li> <li><em>trial:</em> consecutive trial number per subject</li> <li><em>target_label:</em> index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li><em>head_{x,y,z}:</em> head position</li> <li><em>head_quat_{w,x,y,z}:</em> head orientation quaternion</li> <li><em>W_gaze_{x,y,z}:</em> gaze direction</li> <li><em>W_r_hand_{x,y,z}:</em> right hand position</li> <li><em>W_r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>W_l_hand_{x,y,z}:</em> left hand position</li> <li><em>W_l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>W_object_i_{x,y,z}:</em> position of object i</li> <li><em>W_object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li><em>gaze_{x,y,z}:</em> gaze direction</li> <li><em>r_hand_{x,y,z}:</em> right hand position</li> <li><em>r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>l_hand_{x,y,z}:</em> left hand position</li> <li><em>l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>object_i_{x,y,z}:</em> position of object i</li> <li><em>object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> </ul> <p><strong>Acknowledgment:</strong></p> <p>This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.</p>
Overview of surveys on migration aspirations, plans, and intentions.
<p>This is a dataset of metadata on surveys. It is the first comprehensive overview of existing survey data on migration aspirations, plans and intentions, with recorded metadata on geographic and temporal coverage, survey population, sample size, and other characteristics.</p>
Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland
<p>Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.</p>
Data paperhood intentions in adolescents
Open the record for dataset details and reuse information.
Waterloo, Ontario Federal and Provincial Voting Intention and Vaccine Hesitancy
<p>This it the initial release of federal and provincial voting intention in Waterloo Region in the spring of 2022, commissioned by the Laurier Institute for the Study of Public Opinion and Policy.</p>
Core bibliometric Covid19 and comparable research dataset and code for the study "From intent to impact: Investigating the effects of open sharing commitments"
<p>This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix.</p> <p>Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/ </p> <p>Particularly, note that there is an error rate in attribution of signatory status to journal publications and preprints; in their location within specific thematic disease-based areas; or computing of dimension such as identification of data availability statement sections; identification of data depisition mentions within data availability statement sections; or matching of preprints and journal publications.</p> <p>These error rates are expected and have been estimated, please consult the technical report for full details.</p> <p> </p> <p>Definition of data fields is provided is the table below:</p> <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>preprint_server</td> <td>Preprint platform on which a preprint has been published, restricted to arXiv, bioRxiv, medRxiv and SSRN for this study.</td> </tr> <tr> <td>journal_title</td> <td>Publishing journal name in the case of a journal publication.</td> </tr> <tr> <td>year</td> <td>The set is restricted to 2020 and 2021 for Covid19 preprints and journal publications. HVRD journal publications restricted to 2018-2019. HVRD preprints were restricted to 2020-2021 instead, to compensate for the lac of year-normalization for preprints, and generally better control findings against the launch of medRxiv in 2019.</td> </tr> <tr> <td>publication_title</td> <td>Title of the individual journal publication or preprint, not that of the publishing journal or preprint server.</td> </tr> <tr> <td>authors</td> <td>First 100 researchers that appear as authors of a preprint or journal publication. These are not parsed and provided for qualitative validation or assessments rather than for further quantitative treatment.</td> </tr> <tr> <td>Covid19</td> <td>Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>HVRD</td> <td>Human viral respiratory disease, the thematic area considered to be the closest to Covid19. Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>Journal_sig</td> <td>Journal publications where the publishing journal and/or its publishing house are Joint Statement signatories. Coded as 1 if they are signatories, 0 if not signatory, null if status could not be determined due to insufficient metadata. Not that all preprint servers included in this study are Joint Statement signatories. This category was fully removed from the models for preprints, rather than all preprints being assigned automatic signatory status.</td> </tr> <tr> <td>RPO_sig</td> <td>Journal publications and preprints where at least one author is affiliated with at least one research performing organization that is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata.</td> </tr> <tr> <td>Funder_sig</td> <td>Journal publications and preprints where at least one funder supporting the research is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata. Although funding is attributed to researchers rather than publications, funding metadata is more readily available at the second level. This approach also captures the flexible usage of financial resources that researchers may make accross mulitple concurrently ongoing research projects.</td> </tr> <tr> <td>overton_norm</td> <td>Year and subfield-normalized binary score of whether the journal publications has been cited by one or more policy-related documents from the Overton database. Null scores for journal publications not covered by the database.</td> </tr> <tr> <td>overton</td> <td>Normalizations being unable for preprints, binary score of whether the preprint has been cited by one or more policy-ralated documents from the Overton database. Null scores for preprints not covered by the database.</td> </tr> <tr> <td>daswriting_binary</td> <td>Binary score capturing identification of a data availability statement in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>deposition_binary</td> <td>Binary score capturing identification of a data availability statement and data deposition mention therein in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>is_oa</td> <td>Binary score capturing OA or free-to-read (also so-calleod "bronze OA" and "green OA") status of journal publications. Unpaywall categories have been used in a mutually exclusive implementation, with the best (gold > hybrid>bronze>green) possible applicable category being retained. Null scores for journal publications not covered in our Unpaywall dataset. Scores of 0 denote journal publications not available under an OA or free-to-read category.</td> </tr> <tr> <td>is_gold</td> <td>as above</td> </tr> <tr> <td>is_hybrid</td> <td>as above</td> </tr> <tr> <td>is_bronze</td> <td>as above</td> </tr> <tr> <td>is_green</td> <td>as above</td> </tr> <tr> <td>matched_journal_binary</td> <td>For preprints, whether one or more matching journal publications could be identified using the queries identified in the technical, or preprint servers' own lists of preprint-journal publication matches. Null scores for preprints with insufficient metadata information to perform the matching operation.</td> </tr> <tr> <td>matched_journal_doi</td> <td>For those preprints with or more matching journal publications, the DOI(s) of the matching journal publication(s). Note that some of the maching journal publications identified do not have DOIs.</td> </tr> <tr> <td>matched_preprint_binary</td> <td>For journal publications, whether one or more matching preceding preprints could be identified using the queries identified in the technical annex, or preprint servers' own lists of preprint-journal publication matches. Null scores for journal publications without sufficient metadata to run the analysis.</td> </tr> <tr> <td>matched_preprint_id</td> <td>For those journal publications preceded with one or more arXiv, bioRxiv, medRxiv or SSRN preprints, the DOI(s), arXiv ID and/or SSRN ID of the matching preprint(s). </td> </tr> <tr> <td>hasdoi</td> <td>Only journal publications with DOIs were retained in the core quantitative analyses.</td> </tr> <tr> <td>hasacknowledgements</td> <td>Only journal publications with funding acknowledgements (to determine funding-based signatory status) were retained in the core quantitative analyses.</td> </tr> <tr> <td>funder_array</td> <td>Array (but cast as string) of names of the funders on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>RPO_array</td> <td>Array (but cast as string) of names of the research performing organizations on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>DAS_excerpt</td> <td>Journal publication or preprint text excerpt on which succesful identifcation of data availability statements and/or data deposition mentions have been made. Null both where the query could not be run at all, or where the query was negative.</td> </tr> <tr> <td>big5</td> <td>Journal publication published in a journal owned by one of the following five publishing houses: Elsevier, Sage, Springer Nature, Taylor-Francis, Wiley.</td> </tr> <tr> <td>LMIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a lower-middle income country as defined by the World Bank</td> </tr> <tr> <td>LIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a low income country as defined by the World Bank</td> </tr> <tr> <td>SouthNorth</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a upper-middle income country, a lower-middle income country, or a low income country as defined by the World Bank; as well as at least one researcher affiliated with at least one institution located in a high income country. For the purpose of this indicator, Sicnece-Metrix exceptionally includes China and Bulgaria in the list of high income countries.</td> </tr> <tr> <td>DID_allauthors_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_allauthors_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>Preprint_authorcontrol</td> <td>Preprint is included in the the analytical breakdowns where a filter has been applied to control for author-level biases. Note that authors have been kept constant in preprints on the basis of their belonging to all analytical breakdowns in journal publications rather than in preprint-based groups.</td> </tr> </tbody> </table> <p> </p>
Intention to utilize telehealth service in Bangladesh
<p>This data consists of information on participants' knowledge, perceived benefit, perceived concerns, and predispositions related to telehealth services in Bangladesh. In the data set, k1 to k5 indicted the items of knowledge. Similarly, pb1 to pb4 indicate perceived benefit, pc indicates the item of perceived concern, and pd1 to pd2 indicates the items of predisposition. This data set also includes information related to the demographic and perceived health status information. </p>
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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.