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5,526 results for “information”
Geographic Information System for marine aquaculture in Argentina
<p>Planning the use of marine areas for aquaculture through the development of Geographic Information Systems (GIS) has taken on great importance recently . This is because GIS allows decision-making through the analysis and integration of a large amount of data of various kinds gathered in a single database. This system allows the incorporation of information on optimal environmental conditions for farm species and relevant data to develop strategies throughout the entire production chain, from service providers and inputs to the final marketing of the product. The recommended actions of the strategic guidelines for a more sustainable and competitive EU aquaculture in 2021–2030 (EC 2021) stated explicitly the need to “<em>Develop a more detailed guidance document on the planning for space and access to water for marine, freshwater and land-based aquaculture</em>”, highlighting the importance of the GIS.</p> <p>Here you will find 4 files with the following information:<br>1) <strong><em>Metadata.doc</em></strong> file with the details of the metadata used to diagram the GIS layers.<br>2) <em><strong>GIS.gpkg</strong></em> file with each of the layers in raster and vector format.<br>3) <em><strong>Land-based model.gpkg</strong></em> file with examples of GIS modeling for land-based facilities.<br>4) <strong><em>Open-water model.gpkg</em></strong> file with examples of GIS modeling for facilities in open systems.</p> <p> </p>
Improvement of regulations interpretation and formalisation for information need definition - Municipality of Ascoli Piceno, Italy
<p>CHEK Digital Building Permit Maturity Model (CDBPMM) as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit'.</p> <p>(CHEK) https://chekdbp.eu </p> <p>It is described in the CHEK project deliverable D2.1.</p> <p>This project has received funding from the European Union's Horizon Europe program under Grant Agreement No.101058559.</p> <p>The aim of CHEK is to remove barriers preventing municipalities from adopting digital building permit processes by developing, connecting, and aligning scalable solutions in the regulatory and policy context, in open standards and interoperability (geospatial and BIM), in closing knowledge gaps through education, in renewing municipal processes, and in deploying technology. </p>
Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S) (Supporting Information)
<p>Supporting datasets for Howlett et al., "Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S)" in <em>TECTONICS.</em></p>
WiFiCam Dataset | Through-Wall Imaging based on WiFi Channel State Information
<p><strong>Through-Wall Imaging based on WiFi Channel State Information</strong></p> <p>This repository contains the <strong>WiFiCam dataset</strong> for through-wall imaging based on WiFi channel state information proposed in [1]. The corresponding source code repository is located at: <a href="https://github.com/StrohmayerJ/wificam">https://github.com/StrohmayerJ/wificam</a></p> <p>The demo video (demo.mp4) showcases the through-wall imaging capabilities of our approach. </p> <p> </p> <p><strong>Dataset Structure</strong></p> <p>/wificam</p> <p>├── j3</p> <p> └── 320 <-- 320x240 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 92108.png <-- 320x240 RGB image</p> <p> └── 92112.png</p> <p> └── ...</p> <p> └── 640 <-- 640x480 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 154.png <-- 640x480 RGB image</p> <p> └── 155.png</p> <p> └── ...</p> <p>├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images</p> <p>├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] J. Strohmayer, R. Sterzinger, C. Stippel and M. Kampel, "Through-Wall Imaging Based On WiFi Channel State Information," <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 4000-4006, doi: 10.1109/ICIP51287.2024.10647775.</p> <p>BibTeX:</p> <pre>@INPROCEEDINGS{10647775, author={Strohmayer, Julian and Sterzinger, Rafael and Stippel, Christian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Through-Wall Imaging Based On WiFi Channel State Information}, year={2024}, volume={}, number={}, pages={4000-4006}, doi={10.1109/ICIP51287.2024.10647775}}</pre>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response.
<p>The study is part of the large project promoted by WHO Regional Office for Europe called “<em>Monitoring knowledge, risk perceptions, preventive behaviours and trust to inform pandemic outbreak response</em>” and carried out in over 30 countries of the WHO European Region (Registered ISRCTN on 11/05/2021, ID: ISRCTN26200758). In Italy, the survey was conducted administering an online questionnaire developed <em>ad hoc</em> by the WHO in four waves (January-May 2021) to a sample of 10.000 individuals aged 18-70 years. A detailed sampling plan was developed to obtain a representative sample of the Italian adult population. The following variables were taken into account for stratification of the participants: gender by age (four age groups: 18-34 years, 35-44 years, 45-54 years, 55-70 years); geographical area (four areas: North West, North East, Centre, South and Islands); size of living centers (two classes: above and below 100,000 inhabitants); level of education (up to lower middle school, beyond lower middle school); and employment situation (employed, not employed). At the end of each survey’s wave, a weighting procedure has been applied to accurately restore the proportionality of the total sample examined with the reference population, according to the most recent data of the Italian Statistics Institute (ISTAT, 12/31/2019). In particular, data have been weighted for the main socio-demographic and geographic variables (e.g., sex by age by geographical area, occupation, educational qualification, geographical area by size of living centers). The sample size made it possible to maintain a sampling error of less than 2% (at the significance level of 95%) and to control the error of estimates within groups or subgroups of interest. The interviews were conducted by Doxa S.p.a. and carried out with the CAWI technique (Computer Assisted Web Interviewing) on an online panel and on the Confirmit software platform used by Doxa S.p.a. The average administration time was about 18-20 minutes. This study was approved by the Ethics Committee of the IRCCS San John of God Fatebenefratelli of Brescia (n° 72-2020), and all participants provided written informed consent.</p> <p>The primary objectives are to:</p> <p>● Monitor variables that are critical for population behaviour to control transmission of the novel coronavirus, including risk perceptions, knowledge, self-efficacy, confidence in institutions, behaviours, rumours, affect, worry, resilience, trust in/use of information sources and more.<br> ● Document changes over time in these factors to understand the effect of the pandemic process, new developments, events or measures taken.<br> ● Monitor possible issues, e.g. related to misinformation or distrust, as they emerge, to allow early response.<br> ● Identify relationships between variables to identify levers for effective and appropriate responses.<br> ● Explore the relationship of psychological variables (e.g. worry, resilience, trust, affect) with the epidemiological situation and the events and measures taken.<br> ● Identify gaps between perceived and actual knowledge.<br> ● Evaluate the effectiveness of pandemic response measures, and the acceptance and effectiveness of policies and restrictions implemented, including the easing of such restrictions.<br> The secondary objectives are to:<br> ● Contribute to post-outbreak evaluation, thereby contributing to the continued regional/global efforts to better understand mechanisms of crisis response.<br> ● If additional research capacity is available, the data can be triangulated with data on media reporting, COVID-19 cases and other.● If additional research capacity is available, the data can be triangulated with data on media reporting, imported or confirmed cases, etc.: The relationship between psychological variables and characteristics of the outbreak situation can be explored (i.e. how closely the perceived risk mirrors reported cases, relative import risk, media reports).<br> This approach allows a citizen-centred approach where insights into population perceptions and behaviours inform COVID-19 actions, alongside epidemiological data and considerations of economic, cultural, ethical, structural political nature and other.</p> <p>The WHO questionnaire includes 21 different thematic areas noteworthy for the investigation of COVID-19 experience. The questionnaire was translated into specific country language by each recruiting site, following the WHO’s guidelines for translations of tools into other languages. The process included the following steps: forward translation, panel experts, back-translation, pre-test and cognitive interviews and, finally, development of the final version. Variables being surveyed include the following:<br> • Socio-demography;<br> • COVID-19 personal experience;<br> • Health literacy;<br> • COVID-19 risk perception;<br> • Probability and Severity;<br> • Preparedness and Perceived self-efficacy;<br> • Prevention – own behaviours;<br> • Affect;<br> • Trust in sources of information;<br> • Use of sources of information;<br> • Frequency of Information;<br> • Trust in institutions (perceptions);<br> • Policies, interventions (perceptions);<br> • Conspiracies (perceptions);<br> • Resilience (perceptions);<br> • Testing and tracing;<br> • Fairness (perceptions);<br> • Lifting restrictions (pandemic transition phase);<br> • Unwanted behaviour;<br> • Wellbeing;<br> • COVID-19 vaccine.</p> <p> </p>
Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences
<p>Tables of mouse embryonic differential enhancers and promoters.</p> <p>The tables list the genomic coordinates of differential regulatory elements, the tissues in which they are active (labels column), a differential score, and the DNA sequence of the element. Tissues are numbered from 0 to 7, which correspond to heart, kidney, liver, limb, lung, forebrain, midbrain, and hindbrain, respectively. The tool <a href="https://github.com/pbenner/gonetics/tree/master/tools/segmentationDifferential">segmentationDifferential</a> from the <em>gonetics library</em> was used to compute differential elements. The length of all regions was set to 1000 base pairs around the center. Specifics of the experimental data can be found in the referenced publication.</p>
Suppl. Information to "The tropical coral Pocillopora acuta displays an unusual chromatin structure and shows histone H3 clipping plasticity upon bleaching"
<p><strong>Supplementary File 1:</strong> Multiple alignment for protein sequences of core histones with Pocillopora acuta, Pocillopora damicornis, Acropora digitifera, Nematostella vectensis, Hydra vulgaris, Schistosoma mansoni and Mus musculus. A. Histone H2A; B. Histone H2B; C. Histone H3; D. Histone H4. An asterisk (*) means that the amino acid is conserved between all species.</p> <p><strong>Supplementary File 2</strong>: Original (uncropped and unedited) images used for Figures 1 to 4.</p> <p><strong>Supplementary File 3:</strong> <em>P. acuta</em> nuclei and <em>Symbiodinium</em> count on a Thoma cell counting chamber done over three different nuclei extractions. For each extraction, two counts were performed. P. acuta nuclei were stained with Hoechst 33342 and display a blue fluorescence at 350 nm. Symbiodinium are not damaged by our extraction method and are not permeable to Hoechst. They display a red fluorescence because of their chlorophyl content. Observations were done on a Leica DMLB with objective PL Fluotar 40x and 100x. A text version of the data in the Excel file below.</p> <p>Extraction #1 replicate 1: 102 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 112 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 42 <em>P. acuta </em>nuclei (Blue) ; 0 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 55 <em>P. acuta </em>nuclei (Blue) ; 1 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 215 <em>P. acuta </em>nuclei (Blue) ; 3 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 257 <em>P. acuta</em> nuclei (Blue) ; 5 <em>Symbiodinium</em> (Red)</p> <p>Made at IHPE.</p>
Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p>
Bibliographic dataset based on Scientometrics, including provenance information compliant with the OpenCitations Data Model
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals, bibliographic resources, and authors always appear unambiguously, without duplicates. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p> <p>The dataset is distributed as two journal files, one for the data and one for the provenance, readable via the triplestore Blazegraph. There are 4,960,087 data triples and 19,348,027 provenance triples, which corresponds to 1,134,545 entities and 2,696,689 snapshots. Therefore, on average, each entity has two snapshots. Among the data, there are 231,217 agent roles, 221,602 responsible agents, 206,003 bibliographic resources, 142,472 citations, 141,555 bibliographical references, 108,112 identifiers, and 83,584 resource embodiments.</p> <p>The code to generate and modify such collections is available at <a href="https://doi.org/10.5281/zenodo.5579754">https://doi.org/10.5281/zenodo.5579754</a>. </p>
Supplementary Information to: "Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman"
<p>This dataset contains supplementary information required to understand and reproduce the study detailed in our manuscript titled "<em>Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman</em>" which was submitted for publication to Palaeogeography, Palaeoclimatology, Palaeoecology.</p>
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
<p>Datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.</p> <p>Data processed from National Statistical System (INEI) publications.</p> <p>2025.</p>
Market indexes information
<p>Information about <strong>market indexes</strong> scraped from <a href="http://google.com/finance">google.com/finance</a> on November 22st, 2022.</p> <p>The markets studied are:</p> <ul> <li>Market indexes: <ul> <li>Americas</li> <li>Europe, Middle East and Africa</li> <li>Asia Pacific</li> </ul> </li> <li>Most active</li> <li>Gainers</li> <li>Losers</li> <li>Climate leaders</li> <li>Crypto</li> <li>Currencies</li> </ul>
A Reputation Game Simulation: Emergent Social Phenomena from Information Theory
<p>Here, the data underlying the article "A Reputation Game Simulation: Emergent Social Phenomena from Information Theory" (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is provided.<br> <br> The data is structured according to the figures it has been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder "single_simulations_3_agents" (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder "statistical_simulations_3_agents" (Figures 9-13, the upper panel of figure 15, figures 16-18, D2 and the left panels of figure D3)</li> <li>statistical simulations with 4 agents and special communication strategies in the folder "statistical_simulations_4_agents" (Figure 14, the middle panel of figure 15, the middle panels of figure D3 and the upper panels of figures D4, D5)</li> <li>statistical simulations with 5 agents and special communication strategies in the folder "statistical_simulations_5_agents" (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda simulations in the folder "propaganda_simulations" (Figure 7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Bangla Information Retrieval Test Collection | Revisiting Anwesha
<p>There are several IR test collections available in English (e.g. http://ir.dcs.gla.ac.uk/resources/test_collections/). Unfortunately, no Gold standard dataset existed for Bangla IR until recently (https://zenodo.org/record/6583149). Our work expands the existing Gold standard dataset by creating 100 query document relevance pairs across a new test collection of 1000 documents. The corpus contains news articles from Ebela, Zee News and Anandabazar Patrika, Vikaspedia and various Bangla travel blogs. The definition of the complexity level of a query is described below:</p> <p>Complexity Level 1: The query contains exact words, phrases or sentence from the document.</p> <p>Complexity Level 2: The query is not present as it is in the document. There is a slight deviation.</p> <p>Complexity Level 3: The query is a generalised phrase capturing the overall story or the document’s theme.</p> <p>Complexity Level 4: It is a general query not related to any specific document.</p>
Availability of information on citizen science activities, checked against the Activities & Dimensions Grid of Citizen Science on the basis of some projects
<p>The research resulting in this report aimed at answering the following questions:</p> <ul> <li> <p>Which information on citizen science activities is online available that matches the Activity & Dimension Grid of Citizen Science or goes beyond it? </p> </li> <li> <p>Is there any contradictory information?</p> </li> <li> <p>What can be the reason for the availability or non-availability of information about citizen science activities?</p> </li> <li> <p>How does/could this impact on the CS Track’s recommendations?</p> </li> </ul> <p>The corresponding dataset consists of the results of a keyword-based search in the WP2 project database. The information retrieval resulted in 3318 projects on which information is available in German or English.</p> <p>More information on this research can be found in D2.2 section 3.2.</p>
A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)
<p>Publication date:<br> 2022-12-06T07:37:19-06:00</p> <p><br> A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> ---</p> <p>Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth.</p> <p>To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world.</p> <p>At time of writing (6 Dec 2022), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1].</p> <p>This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/current/simple.txt.gz to help facilitate access and improve performance of the creation of search indexes.</p> <p>Pre-process steps currently include:<br> 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name</p> <p><br> Contents<br> ---</p> <p>README:<br> this file</p> <p>repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone.</p> <p>repackage-gbif-backbone.log:<br> log of repackaging of GBIF Simple Backbone.</p> <p>backbone-current-simple.txt.gz:<br> original GBIF backbone archive</p> <p>gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name </p> <p>gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz</p> <p>gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id</p> <p>gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz</p> <p>References<br> ---</p> <p>[1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2022-12-06.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18.</p> <p><br> Hash URIs<br> ---<br> This publication includes the following content uris:</p> <p>hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5<br> hash://sha256/50c155f66efb2efba0b8b624f8541e81cbe16a701d420a5073791fb993f72919<br> hash://sha256/9cd7d4c91292d86c726210446cd6fe45602505a7c0ea3b7c4f4f481f85f193ad (uncompressed)<br> hash://sha256/f950dde25cce9ba9cce67caa1c68ce0c99cb31fe2dc9658fec85a987d9f31654<br> hash://sha256/f21c6b90f17c6083fcfb4853f3c581dcc2aadd291691fa128392a205321f420b (uncompressed)<br> hash://sha256/5e0a4d1d2d1cccbdcc6b2c9831fafe61c54eb055f2d13ec40d9ac161889b9f89<br> hash://sha256/f6e477133d0585706ee5522963b204200cb3cd198f011cbf62be0fa8519763b5 (uncompressed)<br> </p>
Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA (Supporting Information)
<p>Supporting datasets for Ronemus et al., "Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA" in review at <em>Tectonics </em>as of August 16, 2022.</p> <p>Dataset S1. Detailed analytical settings and data for zircon U-Pb geochronology</p> <p>Dataset S2. Biotite <sup>40</sup>Ar/<sup>39</sup>Ar analytic results</p> <p>Dataset S3. Zircon (U-Th)/He analytic results</p> <p>Dataset S4. Apatite and zircon grain photomicrographs with measurements</p> <p>Dataset S5. Apatite (U-Th-Sm)/He analytic results</p> <p>Dataset S6. QTQt input and results files</p> <p> </p> <p>Datasets S1, S2, S3, and S5 are also available in the Tectonics submission supporting information.</p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Survey data on the digitalization of informal businesses in the Global South
<p>During the spring of 2022, the UNDP Accelerator Labs created an online survey to investigate the uptake of digital tools by informal businesses in the Global South. </p> <p>We uploaded the questionnaire onto a digital surveys platform (<a href="https://www.kobotoolbox.org/">https://www.kobotoolbox.org/</a>). We then reached out through UNDP’s network of informal or small businesses in 16 countries, inviting them to complete the questionnaire and spread awareness about it. This implies that respondents are in no way a random sample of the target populations; this choice was made in the interest of speed and cost-effectiveness. We obviously claim no representativity, though we believe that some of our results are strong enough to be considered, to a first approximation, valid as a big picture. </p> <p>1,013 questionnaires from 16 countries, covering all UNDP regions, were completed. In the remaining three countries, we received fewer than 30 completed questionnaire, and decided to discard them. At the country level, the number of respondents ranges from 25 (Ecuador) to 306 (Peru). Our own analysis of the data is published here: <a href="https://doi.org/10.5281/zenodo.7896327">https://doi.org/10.5281/zenodo.7896327</a>.</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.