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1,637 results for “residencies”
North Temperate Lakes LTER: Northern Wisconsin Lake Resident Survey 2005, 2008, 2012
The purpose of this survey was to understand what lake characteristics people value most, what activities they enjoy most, and what they expect for the future of northern Wisconsin lakes. Questions covered aspects such as the property search process individuals went through leading up to their purchase of lakeshore property in Vilas County WI, what activities the individual's household participate in on lakes in Vilas County WI, their attitude about the future of their lake, their perception of the current state of their lake, and lake qualities they would like improved on their lake. Demographics of the respondents and background information about their lake were also collected. Two types of surveying methodologies were used for this survey, one being an internet-based survey, while the other was a mail survey. Surveys were conducted in 2005 and repeated in 2008.
North Temperate Lakes LTER: Northern Wisconsin Lake Resident Survey 2005
The purpose of this 2005 survey was to understand what lake characteristics people value most, what activities they enjoy most, and what they expect for the future of northern Wisconsin lakes. Questions covered aspects such as the property search process individuals went through leading up to their purchase of lakeshore property in Vilas County WI, what activities the individual's household participate in on lakes in Vilas County WI, their attitude about the future of their lake, their perception of the current state of their lake, and lake qualities they would like improved on their lake. Demographics of the respondents and background information about their lake were also collected. Two types of surveying methodologies were used for this survey, one being an internet-based survey, while the other was a mail survey. The dataset includes 1554 observations. Summary of results.
Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities
<p>The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here.</p> <p>Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class). Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. <em>Acacia aneura</em>, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid). </p> <p>The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104. </p> <p> </p> <p> </p>
Resident survey data for Living Lab regions
<p>The datasets present collected data through resident surveys on the perceptions on tourism development and the state of cultural heritage, as part of the Horizon 2020 funded project SmartCulTour (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>).</p> <p>The data is collected <strong>on individual respondent level</strong> for Local Administrative Units (LAUs) for the following municipalities/cities:</p> <ul> <li>Spain: Huesca, Graus, Benasque, Barbastro, Ainsa, Jaca, Sariñena</li> <li>the Netherlands: Rotterdam, Dordrecht, Molenlanden, Ridderkerk, Zwijndrecht, Barendrecht, Delft</li> <li>Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek</li> <li>Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis</li> <li>Finland: Utsjoki</li> <li>Italy: Vicenza, Caldogno, Grumolo, Pojana Maggiore, Lonigo, Montagnana</li> </ul> <p>The data is presented as cross-sectional data and available for the following <strong>year</strong>: 2020.</p> <p>Please consult the <strong>metadata</strong> on each dataset for an overview of collected indicators and units of measurement.</p>
Metacommunity simulations for diatom assemblages residing in benthic cyanobacterial mats in Fryxell Basin in Taylor Valley in the McMurdo Dry Valleys, Antarctica
Here, we use MCSim, a spatially explicit metacommunity simulation package for R, to test alternative hypotheses about the roles of dispersal and species sorting in maintaining the biodiversity of diatom assemblages residing in black and orange mats in Fryxell Basin in Taylor Valley in the McMurdo Dry Valleys of Antarctica. The spatial distribution and patchiness of cyanobacterial mat habitats was characterized by remote imagery of the Lake Fryxell sub-catchment in Taylor Valley collected in January 2015. The available species pool for diatom metacommunity simulation scenarios was informed by the Antarctic Freshwater Diatoms Database, maintained by the McMurdo Dry Valleys Long Term Ecological Research program, representing samples collected between January 1994 and January 2013. We used simulation outcomes to test the plausibility of alternative community assembly hypotheses to explain empirically observed patterns of freshwater diatom biodiversity in the long-term record. The most plausible simulation scenarios suggest species sorting by environmental filters, alone, was not sufficient to maintain biodiversity in the Fryxell Basin diatom metacommunity. The most plausible scenarios included either (1) neutral models with different immigration rates for diatoms in orange and black mats or (2) species sorting by a relatively weak environmental filter, such that dispersal dynamics also influenced diatom community assembly, but there was not such a strong disparity in immigration rates between mat types. The results point to the importance of dispersal for understanding current and future biodiversity patterns for diatoms in this ecosystem, and more generally, provide further evidence that metacommunity theory is a useful framework for testing hypotheses about microbial community assembly. This dataset supports the paper: Sokol, E. Et al, 2020. Evaluating Alternative Metacommunity Hypotheses for Diatoms in the McMurdo Dry Valleys Using Simulations and Remote Sensing Data
Stream tracer experiments to assess channel and hyporheic residence times of streams in the Andrews Experimental Forest in 2001 & 2002
Time series of tracer (Rhodamine WT) concentration data representing a "break-through curve" resulting from a stream tracer injection. Stream tracer experiments were conducted in the lower reach of 2nd-order Watershed 3 in April, 2001, and 2 adjacent reaches of 4th-order Lookout Creek in July, 2002. Rhodamine WT dye was injected as a pulse (non-continuous injection). Concentration data were collected in the field at early time using a field fluorometer equipped with a flow-through cell. Late-time samples collected with an ISCO auto sampler, which were analyzed in the lab with the same fluorometer (reconfigured for analysis with cuvettes) within 72 hours. Particular care was taken to collect late-time, low-concentration data, which are useful in quantifying the residence time of secondary storage within or adjacent to the stream, such as the hyporheic zone or in-stream transient storage zones. In the data set, Tracer 1 refers to the injection in Watershed 3; Tracer 2 refers to the injection in Reach 411 of Lookout Creek; Tracer 3 refers to the injection in the combined Reach 410/411 of Lookout Creek.
SBC LTER: Pore water constituents and residence times (Radon activity) from Santa Barbara beaches, 2012-2013
Constituents of beach pore water and parameters for calculating residence times are reported for two beaches in the Santa Barbara area, Isla Vista Beach and East Campus Beach, from July 2012 to June 2013. This dataset reports beach pore water concentrations of ammonium and nitrate, total dissolved Nitrogen and Carbon, particulate Nitrogen and Carbon, Radon, salinity, conductance, Oxygen and water temperature. Residence time ("Tau") can be calculated from Radon-222 activities in nearshore seawater, in pore water and at equilibrium, which are presented in a second table (also available in published paper). Results from these data were reported in: Goodridge, B. M. and J. M. Melack. 2014. Temporal evolution and variability of dissolved inorganic nitrogen in beach pore water revealed using radon residence times. Environmental Science and Technology, 48: 14211-14218. DOI:10.1021/es504017j
VERTIGO - STARTS Residencies - Public dataset
<p>1 - Summary</p> <p>This archive contains a set of texts produced in the framework of the VERTIGO European project in charge of the STARTS Residencies program. More details about VERTIGO and STARTS Residencies are given in the following section. </p> <p>The STARTS Residencies program of art-science residencies was organised at an unprecedented scale and generated new knowledge which may be useful for further studies. This knowledge concerns the methodology it developed for art-science team building (the process of the call), for ensuring their proper execution through a formalised monitoring approach, and detailed information about each of the residencies and of the concerned stakeholders (Artists, Tech Projects, Producers).</p> <p>These texts are all extracted from the project’s website vertigo.starts.eu and the purpose of this archive is twofold :<br> • to give long-term access to a set of data which is expected to last beyond the life cycle of the vertigo.starts.eu website<br> • to provide additional rights of use beyond the website’s copyright terms which limit the use to access. The terms of the license are specified in an attached document.</p> <p><br> 2 - Introduction to VERTIGO – STARTS Residencies </p> <p>VERTIGO is a Coordination and Support Action (CSA) N°732112 under the European H2020 ICT S+T+ARTS initiative, innovation at the nexus of Science Technology, and the ARTS, supported by the European Commission - DG-CONNECT. STARTS promotes the arts as catalysts for efficient conversion of science and technology knowledge into products, services, and processes. </p> <p>The period of execution of VERTIGO was from December 2016 to May 2020 (42 months). It was managed by a European consortium including the following partners :<br> • IRCAM-Centre Pompidou (Institut de Recherche et Coordination Acoustique/Musique, France) – Project Coordinator : Hugues Vinet (IRCAM)<br> • Fhg-IUK (ICT Group of the Fraunhofer Institute, Germany)<br> • EPFL (Ecole Polytechnique Fédérale de Lausanne, Switzerland)<br> • Inova+ (SME, Portugal)<br> • Artshare (SME, Portugal)<br> • Libelium (SME, Spain)<br> • Association Culture Tech (France)</p> <p>VERTIGO was selected in the framework of the first H2020 ICT Call (ICT36-2016) supporting the STARTS initiative. To achieve its objectives, the STARTS Residencies program managed by VERTIGO has organised and funded artist residencies within Tech Projects - companies, research labs, universities and consortia located in Europe and managing research, development and innovation project, either internal or collaborative. </p> <p>The STARTS Residencies program was organised in 3 yearly open calls for proposals which were selected by an international jury. A total budget of 900.000 € has been allocated for funding the participation of artists in 45 residencies. The selected artists were expected to contribute to the innovative aspects of Tech Projects’ research by bringing original perspectives through artistic practices. Those practices would lead to the production of original artwork based on the project technology and featuring novel use-cases with a high potential for innovation. STARTS Residencies also acted as a platform to showcase produced works to the public and actors of innovation in the framework of various public events and communication actions and through the development of the STARTS starts.eu web site.</p> <p>The three yearly calls for the selection of the residencies stakeholders took place from 2017 to 2019, each of which included two steps: first a call for Tech Projects interested in hosting an artist which resulted in the selection and publication of a list of available Tech Projects, and then a call for artistic residencies based on one Tech project from the list and aiming at producing an artwork based on the Tech Project technology. Artists could apply alone or together with a Producer, an organisation willing to bring additional support (funding, production means) to the residency and the artwork production and dissemination. STARTS Residencies also therefore organised a call for Producers available for considering joint applications with artists. After review of the residencies application by the concerned Tech Projects representatives, an international jury then selected the best STARTS residencies matching the various defined criteria including the congruence with the Tech Project’s expectations, the artistic quality, the technical approach, the potential innovation impact and the relevance of the implementation plan. <br> Once residencies were selected, a process of collaboration was started and coordinated by one representative of VERTIGO-STARTS Residencies, following a monitoring methodology defined for all the residencies. This process, which started with the signature of a co-production agreement between all stakeholders (Tech project, artist(s), VERTIGO representative, optional Producer) included 3 formal meetings (inception, mid-term, completion) with defined deliveries for each of them. </p> <p>VERTIGO also developed the starts.eu web platform as the main matchmaking hub of the STARTS community. All information about VERTIGO and its STARTS Residencies program is presented in the subdomain vertigo.starts.eu. It contains all public information about the selected residencies, the selected Tech Projects and Producers, the presentation of events in which the residencies were presented, as well as all written public documentation (deliverables, brochure, event programs, etc.).</p> <p><br> 3 - Contents of the dataset</p> <p>The dataset contents can be summarised as follows :</p> <p>• a presentation of each of the 45 residencies with one directory per artist, made of the following elements :<br> o The residency portfolio: produced by the VERTIGO partners at the completion of the residency, it provides all factual information (involved stakeholders, the period of execution), abstracts, and a synthesis on the residency: its elements of specificity, its impact in terms of innovation and public information available (events, publications, etc.); <br> o 3 questions to the artist: asked the artist at the beginning of the residency<br> o The residency final public report produced by the artist at the end of the residency</p> <p>• a presentation of the selected Tech Projects, not only the ones involved in the implemented residencies, but also the ones that published their offer to host a residency without an actual implementation as part of STARTS Residencies. These texts were produced following the same template by all the Tech Projects to be included in the list of available Tech Projects for the artistic calls. They include an abstract, a summary of their main challenges, what they expect from the artists and which resources they foresee to bring them. It should be noted that their situation may have evolved from these initial statements and the moment when they hosted a residency. </p> <p>• a presentations of the available Producers: similarly to Tech Projects, texts of presentation of organisations that advertised their availability for co-applying with an artist for an artistic residency.<br> </p>
Scrubbed data on Wikipedians in Residence in Libraries based on the Mapping GLAM-Wiki collaborations
<p><strong>Source</strong>: </p> <p><a href="https://docs.google.com/spreadsheets/d/1UVN-T19g5tE7cONFCkiBkquBJiecoU-w4Rb6F-qR6II/edit#gid=791098161">GLAM-Wiki Activities Mapping - Community Review and Feedback Sheet</a></p> <p><strong>Source's context: </strong></p> <p>Gill, Satdeep. ‘Mapping GLAM-Wiki Collaborations’. <em>This Month in GLAM</em>, March 2020. <a href="https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report">https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report</a>.</p> <p> </p> <p>Data was scrubbed using <a href="https://openrefine.org/download.html">OpenRefine 3.4.1</a></p> <p>The original spreadsheet had only partial information in many fields and it is a work in progress (for more see the "source's context" link above).</p> <p>I have only manually double checked those rows in which the “Primary partner institution” contains the stem “libr*” or “bibli*. The following eight rows where modified and “Library” was added in the “Type of institution” column: Municipal Library, Patiala, BRAU Library of the University of Naples Federico II, Library and Archives Canada, Eötvös Loránd University Library and Archives, National Health Library and Knowledge Service, National Doctors Training and Planning, Daniel Cosío Villegas Library, Cantonal and University Library, Nationaal Archief | Koninklijke Bibliotheek; and “Library association” was added to the Online Computer Library Center (OCLC) entry. All changes can be seen in the <a href="https://zenodo.org/api/files/ae475409-a2a8-4e51-98e2-68b3090d0fd0/WiRs-in-libraries_MGW_scrubbing-changes.json">WiRs-in-libraries_MGW_scrubbing-changes.json</a> file in this release.</p>
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
eSense Smart Home: A PIR-based solo-resident Smart Home dataset
<p>In the evolving landscape of smart homes, there is a rising interest in making homes not just smarter but also more suitable for elderlies, in particular, to mitigate the challenges of ageing problem. This is the main motivation, which sheds light on the importance of creating and testing smart systems. In this situation, access to datasets collected from real-life activities is crucial for coming up with new and better ways to improve the systems and solutions developed for smart homes. The eSense Smart Home dataset is collected from a solo-resident smart home testbed equipped with a network of Passive Infrared (PIR) sensors. The sensors are strategically placed to monitor doorways and living areas, capturing detailed insights into occupant's movements and transitions within the house. The data collection was conducted in two rounds with two different setups for sensor placement in the testbed. This dataset offers a valuable resource for researchers and practitioners interested in understanding user behaviour within a home environment and includes raw sensor readings from different areas of the home and a log representing the time spent in each room. More detailed information regarding data collection and event logs are presented in the document that is included within the dataset files.</p>
Diverse baleen whale acoustic occurrence around two sub-Antarctic Islands: A tale of residents and visitors
<p>This dataset contains the acoustic .wav file of all exemplar calls illustrated by the spectrograms in the manuscript figure, MS Excel Spreadsheet file with baleen whale call occurrence and environmental data, and the R code used for fitting the RF models. R codes must be run in the following manner:</p> <p>1. 01_tune_occ_enviro_rf_model_balance_baleen_whales</p> <p>2. 02_process_occ_enviro_rf_model_balance_baleen_whales</p> <p>The codes are self-explanatory given the comments contained therein, and the source code for fitting the codes is provided as 000_source_all.</p>
Perceptions of green facades among residents of buildings with and without a greened envelope – Data from a household survey in Leipzig, Germany
<p>The data set stems from a survey of residents in two neighborhoods of Leipzig, Germany, and was implemented in April and May of 2022. The primary aim of the study was to better understand resident perceptions of green facades, including their (perceived) benefits as well as concerns. Additionally, residents were asked for a number of other perceptions, including heat stress, noise and air pollution. The sample includes both residents of buildings with and without an existing green facade.</p> <p>All variables included in this data publication are described in the codebook. The original German language wording of the survey questions can be found in the questionnaire enclosed with the data set. We include responses to all questions from the survey that were close-ended or had a numerical response. Open-ended questions were excluded from this publication for data privacy reasons. </p>
VCM Dataset for the Classification of Resident Space Objects
<p>The Vector Covariance Message (VCM) data comprise 22,303 RSOs over a period of six months (9/1/2022-2/28/2023). VCM data consist of Resident Space Objects (RSOs) ephemerides from a high-precision special perturbations orbit propagator and estimator using tracking observations. VCMs are issued by the US Space Force (USSF) Space Command (USSPACECOM) and were provided through an Orbital Data Request (ODR) the authors submitted to the 18th Space Defense Squadron (18th SDS). </p> <p>The dataset is organized into subfolders, each containing VCMs for a specific satellite. Filenames correspond to the satellite's NORAD ID (North American Aerospace Defense Catalog Number). A readme file provides details about the VCM content and format. Note that the full covariance matrix has been excluded for public release, whereas the standard deviation of error in satellite's position and velocity is provided.</p> <p>The VCM data have been used in the following work, "Early Classification of Space Objects based on Astrometric Time Series Data", presented at the 25th Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in Maui, Hawaii, United States.</p>
Activity based synthetic population of residents for Gothenburg, Sweden
<p>A synthetic population is a distribution of synthetic agents that replicates the demographic distribution of a real-world population according to census records.<br>This dataset contains a synthetic population of residents in the city of Gothenburg in Sweden, along with activity schedules and mobility patterns for 2019. The synthetic population model is designed for applications in neighbourhood planning and includes detailed replicas of people in different neighbourhoods of Gothenburg organized as persons, households, houses, buildings, and daily activity chains. While the persons, households, and houses are synthetic replicas, they are connected to existing buildings.<br>The model considers the allocation of primary and secondary locations based on a gravity model, realistic routing for active, public and private motorised modes of transportation and allows users to introduce new buildings and amenities if needed. The population data is provided as an SQLite3 database file for each neighbourhood of Gothenburg.</p>
Preparing educational presentation about therapeutic exercises to resident physiatrists
<p>This study evaluated the effectiveness of ChatGPT-4o, a generative artificial intelligence (AI) platform, in preparing educational presentation on therapeutic exercises specifically designed for physiatry residents. Both a physiatry expert and ChatGPT-4 created PowerPoint slides for a presentation on therapeutic exercises using the same reputable sources. Two other physiatry experts, blinded to the origin of the presentation and each other's scores, independently assessed both presentations using four of the CLEAR criteria (completeness, lack of false information, appropriateness, and relevance).</p> <p>Statistical analyses confirmed the interrater reliability. The average scores of the expert-prepared slides were significantly higher than those of the AI-prepared slides. However, when assessing the presentations as a whole, no statistically significant difference emerged between the average AI and expert scores. The overall scores of the AI-prepared slides were rated between good and excellent, while the expert-prepared slides were rated as excellent. Similarly, when assessing the presentations as a whole, the AI performed good whereas expert performed excellent. Upon evaluating the variability within the model for the assessed criteria, it was found that the AI ranked highest in relevance, whereas the expert ranked highest in terms of lack of false information.</p> <p>These findings indicated that although ChatGPT-4o can produce effective educational content, the expert still outperformed AI. This underscores the importance of professional oversight in maintaining the educational quality of resident physician training. Furthermore, fostering collaboration between humans and AI can lead to enhanced educational outcomes in the field.</p>
Synthetic multi-day activity-travel schedules for Swedish residents
<div> <h2><strong>About </strong></h2> <p>This dataset contains multi-day activity-travel schedules for <strong>over 263,000 individuals residing in Sweden</strong>, representing approximately <strong>2.6% of country's population</strong>. The individuals and their daily schedules are derived from mobile phone application data covering seven months in 2019. Mobile phone application data, one example of emerging mobility data sources, offers an alternative to other data collection methods. This data is collected by capturing phone users' geographical locations with their consent as they interact with various mobile applications. </p> </div> <div> <p>This open data repository includes activity-travel schedules for each individual <strong>over five simulated average weekdays</strong>, <strong>incorporating daily variability at the individual level</strong>. <strong>Each simulation day provides:</strong> </p> </div> <div> <ul> <li> <p><strong>Anonymized Identifiers:</strong> Unique IDs that link individuals across all simulation days. </p> </li> </ul> </div> <div> <ul> <li> <p><strong>Activity Locations:</strong> Locations for home, work/school and other activities. </p> </li> </ul> </div> <div> <ul> <li> <p><strong>Daily Activity-Travel Schedules:</strong> Detailed information on activity sequence, type, start and end times, and locations. </p> </li> </ul> <p> </p> <div> <h2><strong>Background </strong></h2> </div> <div> <p>The activity-travel schedules were created using a novel generative model that synthesizes individuals' average weekday activity-travel schedules from mobile phone application data. Mobile data provides geographically and population-wise extensive observations over extended periods, offering valuable insights into individuals' whereabouts. However, these datasets often include sampling biases in the population coverage and individual-level data sparsity due to intermittent and irregular phone application activities, from which the underlying geolocation data were passively collected. </p> </div> <div> <p>The generative model combines mobile data with the Swedish national travel survey [1]. The model employs state-of-the-art primary activity identification methods to infer individuals’ primary activity locations, i.e., home and work/school snapped to buildings. The proposed model can generate multiple schedules for each individual, showing activity sequences, types, start/end times and locations, incorporating daily variability in specific schedule attributes. At the individual level, variations occur across all elements of activity schedules, i.e., activity sequences, type, start/end times, and other activity locations, while maintaining the residential and workplace locations. Moreover, the model calculates a weight for each individual based on their residential location and inferred employment status, addressing sampling biases and ensuring a representative sample of the Swedish population. </p> </div> <div> <p>The performance of the generative model is evaluated by comparing its synthesized activity-travel schedules with those from <a href="https://www.sciencedirect.com/science/article/pii/S2352340923003281?via%3Dihub" target="_blank" rel="noopener">the SySMo model</a> [2], large-scale agent-based model of Sweden and with underlying travel survey data. The results demonstrate that the proposed model effectively addresses biases and sparsity in mobile phone application data, resulting in realistic and reliable activity-travel schedules. The pre-print paper "<a href="https://arxiv.org/abs/2410.22386" target="_blank" rel="noopener">Mobile Phone Application Data for Activity Plan Generation</a>" details the model's methodology and evaluation.<br><br></p> <div> <h2><strong>Data Description </strong></h2> </div> <div> <p>The current data covers 5 data files, each showing a simulation day. </p> </div> <div> <div> <div> </div> <table> <tbody> <tr> <td> <div> <div> <p>Column </p> </div> </div> </td> <td> <div> <div> <p>Description </p> </div> </div> </td> <td> <div> <div> <p>Data type </p> </div> </div> </td> <td> <div> <div> <p>Unit </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>PId </p> </div> </div> </td> <td> <div> <div> <p>Unique Anonymized Identifiers </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>employment </p> </div> </div> </td> <td> <div> <div> <p>Employment Status (0 = Not Employed, 1 = Employed) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>weight </p> </div> </div> </td> <td> <div> <div> <p>Weight showing the representativeness of the individuals </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_id </p> </div> </div> </td> <td> <div> <div> <p>Activity index of each agent </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_purpose </p> </div> </div> </td> <td> <div> <div> <p>Activity purpose (work/ home/ other) </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_start </p> </div> </div> </td> <td> <div> <div> <p>Start time of activity in minute (0-1439) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>minute </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>act_end </p> </div> </div> </td> <td> <div> <div> <p>End time of activity in minute (0-1439) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>minute </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_x </p> </div> </div> </td> <td> <div> <div> <p>Coordinate X of activity location (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>meter </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_y </p> </div> </div> </td> <td> <div> <div> <p>Coordinate Y of activity location (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>meter </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_lat </p> </div> </div> </td> <td> <div> <div> <p>Latitude of activity location (WGS 84) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>degrees </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>point_lng </p> </div> </div> </td> <td> <div> <div> <p>Longitude of activity location (WGS 84) </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>degrees </p> </div> </div> </td> </tr> </tbody> </table> </div> </div> <div> <h2> </h2> <h2><strong>Privacy Policy </strong></h2> </div> <div> <p>The data underlying this study were purchased from PickWell and are subject to restrictions due to licensing and privacy considerations under the European General Data Protection Regulation (GDPR). Therefore, these data are not publicly available but can be requested for research purposes through commercial access. We adhere to the guidelines established by the Chalmers Institutional Review Board (IRB) following the Swedish Ethical Review Act (2003:460) and GDPR 2016/679. The dataset contains no personal information traceable to individuals. Geolocations in this dataset are synthesized from empirical mobile application data, ensuring privacy while retaining their utility for studying mobility behavior and simulating large-scale travel demand. </p> </div> <div> <p> </p> <h2><strong>Acknowledgement</strong> </h2> </div> <div> <p>This research is funded by the Swedish Research Council Formas (Project Number 2018-01768). The authors acknowledge Sonia Yeh for her intellectual contributions to the study. Additionally, the authors sincerely thank Jorge Gil for providing the mobile phone application data. </p> <div> </div> </div> <p> </p> </div> </div>
Data to support Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays"
<p>Datasets supporting the manuscript: Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays". <em>BMC Ecology and Evolution </em><strong>23</strong>, 31 (2023). https://doi.org/10.1186/s12862-023-02130-0</p> <p>Nuclear data:</p> <p>"Mobula-alfredi_nuclear_reference_RAD_contigs.fasta" is a fasta of 359,751 contigs that serve as the reference for nuclear alignment of genotypes to RAD loci. Contigs begin and end with GATC cut site.</p> <p>Mobula-alfredi_nuclear_all_2048snps_38genotypes.vcf is a VCF file with all 2048 nuclear SNPs in final filtered SNP dataset. 38 genotypes are included from Maui Nui and Hawaii Island. This 2048 SNPs includes both 2038 neutral and 10 outlier SNPs. </p> <p>Mobula-alfredi_nuclear_neutral_2038snps_38genotypes.vcf is a VCF file with 2038 neutral nuclear SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_nuclear_outliers_10snps_38genotypes.vcf is a VCF file with 10 outlier SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Structure (.str) files are also provided in addition to VCFs. In all files Population prefixes M=Maui Nui and K=Hawaii Island. </p> <p>Mitochondrial data:</p> <p>Mobula-alfredi_mitogenome_34haplotypes_9sites_min4x.vcf is a VCF file with 9 variant sites across the mitogenome haplotyped in 34 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_mitogenome_34haplotypes_allsites_min4x.fasta is a FASTA file with whole mitogenomes aligned to OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409]. Sites with less than 4x coverage were masked with Ns. </p> <p>Mobula-alfredi_mitogenome_reference_OP562409.fasta is a FASTA file containing the <em>Mobula alfredi</em> reference mitogenome OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409].</p> <p> </p>
Supplementary data for "Influence of prey availability on habitat selection during the non-breeding period in a resident bird of prey"
<p><strong>Abstract</strong></p> <p>Background: For resident birds of prey in the temperate zone, the cold non-breeding period can have strong impacts on survival and reproduction with implications for population dynamics. Therefore, the non-breeding period should receive the same attention as other parts of the annual life cycle. Birds of prey in intensively managed agricultural areas are repeatedly confronted with unpredictable, rapid changes in their habitat due to agricultural practices such as mowing, harvesting, and ploughing. Such a dynamic landscape likely affects prey distribution and availability and may even result in changes in habitat selection of the predator throughout the annual cycle.</p> <p>Methods: In the present study, we 1) quantified barn owl prey availability in different habitats across the annual cycle, 2) quantified the size and location of barn owl breeding and non-breeding home ranges using GPS-data, 3) assessed habitat selection in relation to prey availability during the non-breeding period, and 4) discussed differences in habitat selection during the non-breeding period to habitat selection during the breeding period.</p> <p>Results: The patchier prey distribution during the non-breeding period compared to the breeding period led to habitat selection towards grassland during the non-breeding period. The size of barn owl home ranges during breeding and non-breeding were similar, but there was a small shift in home range location which was more pronounced in females than males. The changes in prey availability led to a mainly grassland-oriented habitat selection during the non-breeding period. Further, our results showed the importance of biodiversity promotion areas and undisturbed field margins within the intensively managed agricultural landscape. </p> <p>Conclusions: We showed that different prey availability in habitat categories can lead to changes in habitat preference between the breeding and the non-breeding period. Given these results we show how important it is to maintain and enhance structural diversity in intensive agricultural landscapes, to effectively protect birds of prey specialised on small mammals. Hereafter we provide the datasets and R script to reproduce the resource selection functions.</p>
Dataset related to article "NKp46-expressing human gut-resident intraepithelial Vδ1 T cell subpopulation exhibits high antitumor activity against colorectal cancer"
<p>γδ T cells account for a large fraction of human intestinal intraepithelial lymphocytes (IELs) endowed with potent antitumor activities. However, little is known about their origin, phenotype, and clinical relevance in colorectal cancer (CRC). To determine γδ IEL gut specificity, homing, and functions, γδ T cells were purified from human healthy blood, lymph nodes, liver, skin, and intestine, either disease-free, affected by CRC, or generated from thymic precursors. The constitutive expression of NKp46 specifically identifies a subset of cytotoxic Vδ1 T cells representing the largest fraction of gut-resident IELs. The ontogeny and gut-tropism of NKp46+/Vδ1 IELs depends both on distinctive features of Vδ1 thymic precursors and gut-environmental factors. Either the constitutive presence of NKp46 on tissue-resident Vδ1 intestinal IELs or its induced expression on IL-2/IL-15-activated Vδ1 thymocytes are associated with antitumor functions. Higher frequencies of NKp46+/Vδ1 IELs in tumor-free specimens from CRC patients correlate with a lower risk of developing metastatic III/IV disease stages. Additionally, our in vitro settings reproducing CRC tumor microenvironment inhibited the expansion of NKp46+/Vδ1 cells from activated thymic precursors. These results parallel the very low frequencies of NKp46+/Vδ1 IELs able to infiltrate CRC, thus providing insights to either follow-up cancer progression or to develop adoptive cellular therapies.</p> <p> </p> <p>This dataset is created with fcs files form, in order to guarantee the access we attach a pdf information about</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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
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