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Framework for virtual education of COVID-19 vaccines for Mandarin-speaking learners: An educational intervention module
<p><strong>Background</strong>: In the United States, patients with limited English proficiency face significant barriers to comprehending and acting upon health-related information, particularly during the COVID-19 pandemic. The ability of health professionals to communicate COVID-19-related information to Mandarin-speaking patients has proved critical in discussions about vaccine efficacy, side effects, and post-vaccine protection.</p> <p><strong>Methods</strong>: The authors created a one-hour educational module to help Mandarin-speaking medical students better convey COVID-19 vaccine information to Mandarin-only speakers. The module is composed of an educational guide, which introduced key terminology and addressed commonly asked questions, and pre- and post-surveys. The authors recruited 59 Mandarin-speaking medical students from 31 U.S. academic medical centers, all of whom had previously completed a medical Mandarin elective. The module and surveys were distributed and completed in August 2021. Data analysis measured the change in aggregate mean for subjective five-point Likert-scale questions and change in percent accuracy for objective knowledge-based questions.</p> <p><strong>Results</strong>: The educational module significantly improved participants' subjective comfort level in discussing the COVID-19 vaccine in English and Mandarin. The largest improvement in both English and Mandarin was demonstrated in the participant's ability to explain differences between the COVID-19 vaccines, with an aggregate mean improvement of 0.39 for English and 1.48 for Mandarin. Survey respondents also demonstrated increased percent accuracy in knowledge-based objective questions in Mandarin.</p> <p><strong>Conclusions</strong>: This module provides Mandarin-learning medical students with skills to deliver reliable information to the general population and acts as a model for the continued development of educational modules for multilingual medical professionals.</p>
EVALUATION OF EDUCATIONAL POLICIES FOR EMERGENCY REMOTE EDUCATION AND THE POST-PANDEMIC: A STUDY BASED ON THE LEGAL FRAMEWORKS OF THE STATE SCHOOL SYSTEM OF RORAIMA, BRAZIL
<p>This article provides an assessment of the legal framework built during the social isolation phase of the COVID-19 pandemic, by the Federal Government and the State Government of Roraima, regarding the provision of Emergency Remote Teaching (ERE). This is a research with a qualitative approach, developed through document analysis and bibliographical research, with the selection and categorization of all educational regulations issued by the Union and the state of Roraima in the years 2020 and 2021, as well as articles that deal with of the theme of ERE in the Scielo database and in the Capes Catalog of Theses and Dissertations. The normative instruments that sought to guarantee the right to education of children, young people and adults in basic education when school attendance was prohibited were highlighted. Among the main conclusions, it was found that the lack of infrastructure and technological knowledge of school communities were obstacles, as the bibliography shows, even though legally there was a significant effort from the Roraima Government, to guarantee autonomy to schools in offering ERE.</p>
Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"
<p>This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework".</p> <p>Below are the details of which file is required to produce which figure:</p> <p> </p> <p><strong>Figure 5 (De_dam_fields.pdf) </strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a></p> <p> </p> <p><strong>Figure 6 (convergence.pdf)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a></p> <p>Contains 4 files, each one for a different temporal resolution (delta t).</p> <p> </p> <p><strong>Figure C1 (convergence2.pdf, appendix)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a></p> <p>Each contains 4 files, one for each temporal resolution (delta t).</p> <p> </p> <p><strong>Figure 7 (CPU_time.pdf)</strong></p> <p>No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.</p> <p> </p> <p><strong>Figure 8 (T_h.pdf)</strong></p> <p><strong>Left panels, a, c, e</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a></p> <p>Each contains 4 files, one for each healing time (T_h)</p> <p><strong>Right panels, b, d, f</strong></p> <p>comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)</p> <p> </p> <p><strong>Figure 9 (comp_ddam_We_0_001.pdf)</strong></p> <p>comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 10 (comp_ddam_We_0_1.pdf)</strong></p> <p>comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 11 (discussion.pdf)</strong></p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz</p> <p> </p> <p><strong>SI movie</strong></p> <p>SI_movie.tar.gz</p>
Supporting material for PyESDv1.0.1 An open-source Python framework for empirical-statistical downscaling of climate information
<p>The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (<em>pyESD; </em>github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of code. The package is highly modular and flexible and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even glacial retreat. The dataset presented here serves as supporting material for the package description and evaluation manuscript</p>
Survey data of an integrative evaluation framework for assessing the sustainability of different types of urban agriculture
<p>In this dataset we present core data of an integrative evaluation framework for assessing the environmental, social, and economic sustainability of urban agriculture. The multi-criteria analysis is conducted by an Analytic Hierarchy Process and a participatory approach. The data integrate the selection and weighting of sub-criteria based on two online surveys:</p> <p>1) Survey 1: The selection of suitable sub-criteria for assessing the sustainability of urban agriculture was done by European scientific experts.</p> <p>2) Survey 2: The weighting of the selected sub-criteria was done on the example of vertical farming and community supported agriculture. Therefore, we involved stakeholders representing key actors for the implementation of urban agriculture: city administrations and non-governmental organizations (NGOs) of ten German case study cities, practitioners and technical-scientific experts.</p> <p> </p> <p><strong>List of data and content</strong></p> <p>1) Survey_1 (*.zip):</p> <ul> <li>Survey_1_Criteria_Selection_English: Online survey in English (*.pdf)</li> <li>Survey_1_Information_Sub-criteria_English: Information about the sub-criteria provided in the survey (in English) (*.pdf)</li> <li>Survey_1_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> </ul> <p>2) Survey_2 (*.zip):</p> <ul> <li>Survey_2_AHP_City_Administrations_German: Online survey for city administrations in German (*.pdf)</li> <li>Survey_2_AHP_Practitioners_German: Online survey for practitioners and technical-scientific experts in German (*.pdf)</li> <li>Survey_2_AHP_NGOs_German: Online survey for NGOs in German (*.pdf)</li> <li>Survey_2_Information_Sub-Criteria_German: Information about the sub-criteria provided in the survey (in German) (*.pdf)</li> <li>Survey_2_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> <li>rdata_CA_AHP_edible_Cities_2022-03-18_10-28: Results of the survey for city administrations (*.csv)</li> <li>rdata_NGO_AHP_edible_Cities_2022-03-18_10-40: Results of the survey for NGOs (*.csv)</li> <li>rdata_PE_AHP_edible_Cities_2022-03-18_10-41: Results of the survey for practitioners and technical-scientific experts (*.csv)</li> <li>rdata_all_AHP_edible_Cities_2022-03-18_09-53: Total results of the survey</li> </ul> <p> </p> <p><strong>Data acquisition and processing</strong></p> <p>The methods are described in this linked publication:</p> <p><span>John, H., & Artmann, M. (2024). Introducing an integrative evaluation framework for assessing the sustainability of different types of urban agriculture. </span><span>I<em>nternational Journal of Urban Sustainable Development, </em></span>16 (1), 35-52<em><span>. </span></em><span>doi:<em> </em>10.1080/19463138.2024.2317795</span></p> <p>The methodology of the performed analytic hierarchy process (AHP) is published in a separate repository on GitHub including a paper that systematically explains the AHP by means of code examples, starting with the raw data, through their adaptation to the software functions of the ahpsurvey R-package, and finally, execution of the AHP up to the visualization of the results.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank Mabel Killinger and Marie Herzig for their help in stakeholder identification as well as all experts and stakeholders for their participation in the two online surveys and their helpful comments. Data processing and analysis by means of an Analytic Hierarchy Process in R would not have been possible without the help of Björn Kasper.</p>
Supporting datasets for "Dynamical Equilibrium between Brønsted and Lewis Sites in Zeolites: Framework-Associated Octahedral Aluminum"
<p>Tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including CHA and MOR in H form, various Si/Al ratios and water loadings.</p>
A laboratory-scale simulation framework for analyzing wildfire hydrologic and water quality effects
<p>Datasets containing information on experimental conditions for each tested soil sample, measured hydrologic and water quality responses, and calculated response metrics.</p>
Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
<p>Sandy beaches provide essential nesting habitat for sea turtles but are threatened globally by a rapidly changing climate. Identifying which nesting sites are at greatest risk from erosion and inundation remains an important goal of sea turtle conservation globally. Yet, efforts to identify at-risk sites have been hindered by the ability to model complex processes and incomplete information on nesting distribution and abundance. To assess the erosion and inundation risk to the reproductive success of a discrete genetic stock of flatback turtles (<em>Natator</em> <em>depressus</em>) across its nesting range in the Pilbara region of Western Australia, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Coastal Vulnerability Model. A relative exposure index was calculated for 402 nesting beaches in terms of six geophysical variables: wind and wave exposure, surge potential, relief, observed sea level rise and coastal geomorphology, and coupled with published information on the distribution and abundance of turtle tracks in the region. </p> <p>The majority of beaches (74%) had an intermediate to high exposure. In particular, 36% of beaches with a high abundance of flatback tracks (the top 25% of the frequency distribution) had a high exposure (the top 25% of the frequency distribution). This suggests that coastal exposure is a key vulnerability to the reproductive success of sea turtles that nest in this region. Promisingly, five beaches with a high abundance of turtle tracks also had a low exposure (bottom 25% of the frequency distribution) and these beaches may be critical for the long-term resilience of the stock against sea level rise and severe storms. Exposure varied across nesting sites and the approach presented here allows for a rapid and broadscale assessment of relative erosion and inundation risks at a scale most relevant to management. </p>
An Empirical Study of Vulnerabilities in Edge Frameworks to Support Security Testing Improvement
<p>This package contains the material collected to perform the empirical study described in our paper entitled ‘An Empirical Study of Vulnerabilities in Edge Frameworks to Support Security Testing Improvement’ authored by Jahanzaib MALIK (<a href="mailto:jahanzaib.malik@uni.lu">jahanzaib.malik@uni.lu</a>) and Fabrizio PASTORE (fabrizio.pastore@uni.lu).</p> <p> </p>
Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings - dataset
<p>This dataset accompanies the conference submission 'Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings'. It contains the data in CSV format, one file per unit of analysis (Units of Assessment, Higher Education Institutions, and Subject Areas).</p> <p>The format of the UoA file is as follows (the other two files are analogous):</p> <ul> <li> <p>institution_name: name of higher education institution (e.g. university)</p> </li> <li> <p>unit_of_assessment_name: UoA name in REF (https://www.ref.ac.uk/panels/units-of-assessment/)</p> </li> <li> <p>main_panel: main panel in REF</p> </li> <li> <p>multiple_submission_letter: blank unless submitted to multiple panels.Exceptionally HEIs may have requested permission to make <a href="https://ref.ac.uk/publications-and-reports/invitation-to-make-requests-for-multiple-submissions-exception-from-submission-for-small-units-and-for-impact-case-studies-requiring-security-clearance/">two submissions from the same UoA to different panels</a>.</p> </li> <li> <p>multiple_submission_name: blank unless submitted to multiple panels</p> </li> <li> <p>non_english: number of articles in language other than English</p> </li> <li> <p>jufo_score_uoa: JUFO score (see paper for calculation details) of UoA</p> </li> <li> <p>jif_score_uoa: JIF score (see paper for calculation details) of UoA</p> </li> <li> <p>ref_score_uoa: REF score (see paper for calculation details) of UoA</p> </li> </ul>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
Information organizing: an evolutionary and development framework
<p>Information behaviour has emerged as an important aspect of human life; however, our<br> knowledge and understanding of it is incomplete and underdeveloped scientifically. Our<br> understanding of information behaviour and the sub-process information organizing<br> behaviour is largely contemporary in focus. In this presentation Professor Spink discusses<br> an evolutionary and developmental framework for information behaviour and information<br> organizing from her recent book Information Behaviour: An Evolutionary Instinct (2010,<br> Springer) by incorporating related findings, theories and models from evolutionary<br> psychology, cognitive archaeology, cognitive neuroscience and developmental psychology.<br> In her presentation, she argues that information behavior and information organizing are<br> important instinctive socio-cognitive abilities for all humans that can only be fully under-<br> stood with an evolutionary and developmental perspective.<br> Professor Spink’s presentation addresses four important research questions. Firstly, what is the evolutionary, biological and developmental basis for information behaviour and information organizing behaviour? Secondly, what is the role of instinct versus environment in shaping information behaviour and information organizing behaviour? Thirdly, how have information behaviour and organizing capabilities evolved and developed over human species? Fourthly, when and how do information behaviour and in particular information organizing abilities emerge in young children? An evolutionary and developmental approach lays the foundation for a more holistic perspective on information behaviour and information organizing behaviour, and opens many new research directions.</p>
A metadata framework for electronic phenotypes
<p>As many phenotyping algorithms are being created to support precision medicine or observational studies using electronic patient data, it is getting increasingly difficult to identify the right algorithm for the right task. A metadata framework promises to help curate phenotyping algorithms to facilitate more efficient and accurate retrieval. We recruited 20 researchers from two phenotyping communities, the eMERGE and the OHDSI communities, and used a mixed-methods approach to develop the metadata framework. Once we achieved a consensus of 39 metadata elements, we surveyed 47 new researchers from these communities to evaluate the utility of the metadata framework. Two researchers were also asked to use it to annotate eight type 2 diabetes mellitus phenotypes. The survey consisted of a series of multiple-choice questions, which allowed rating of the utility of each element on a scale of 1-5, and open-ended questions, which allowed for narrative responses. More than 90% of respondents rated metadata elements concerning phenotype definition and validation methods and metrics with a score of 4 or 5. Our thematic analysis of the respondents' feedback indicates that the strengths of the metadata framework were its ability to capture rich descriptions, explicitness, compliance with data standards, comprehensiveness in validation metrics, and ability to enable cross-phenotype searches. Limitations were its complexity for data collection and entailed costs.</p>
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
<p>News headline to event classes corpus derived from Wikidata.</p> <p>Github: https://github.com/mbouadeus/news-headline-event-linking</p>
Dataset for "Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning"
<p>Dataset for "Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning", codes can be found <a href="https://github.com/GuzhongChen/ILTransR">here</a>.</p>
Dataset of the Article Evaluating a Framework of Conceptual Modelling Research through a Family of Experiments
<p>Data set of the article "Evaluating a Framework of Conceptual Modelling Research through a Family of Experiments". Contains the results of:</p> <p>-Demographic questionnaire</p> <p>- Agreement and correctness of Paper A</p> <p>- Agreement and correctness of Paper B</p> <p>- Agreement and correctness of Paper C</p> <p>-Satisfaction questionnaire</p> <p>-Procedure and Pros and Cons of the framework</p>
Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks
<p>Dataset used in the publication of Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks. The paper is published at <a href="https://www.nature.com/articles/s41524-023-01125-1">npj Computational Materials</a> (https://www.nature.com/articles/s41524-023-01125-1)</p> <p> </p> <p><strong>May 19, 2023:</strong></p> <p>- Updated the format in CSV files to be comma-separated.</p> <p>- Updated the building block labels.</p> <p>- Updated README.txt to include additional information.</p>
Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework
<p>Input files, data files and scripts to replicate results in "Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework" (Jambunathan et el.)</p> <p> </p> <p>Please direct any questions to the corresponding author, Revathi Jambunathan (rjambunathan [at] lbl.gov)</p> <p>Artemis simulations were run using the development branch of artemis <a href="https://github.com/ECP-WarpX/artemis">https://github.com/ECP-WarpX/artemis </a></p> <p>Some simulations in this paper were performed with different commit hashes of artemis and amrex, however, the input files should still work with the most recent development branch of artemis.</p> <p>In the attached tar file, the input files, data, and scripts used to analyse simulation results shown in Figures 1, 2, 3, 4, and 6 of the paper are provided. To run the simulations on perlmutter GPUs, the code is compiled with USE_GPU=TRUE and USE_LLG=FALSE</p> <p> </p> <p> </p> <p> </p>
Timelapse of Outdoor Pre-experiment of Monitoring Framework
<p>The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The timelapse is a pre-experiment of the Outdoor Monitoring Framework developed for investigating the behaviour of bio-based materials to weathering. </p>
Sensed Data of Indoor Monitoring Framework in Aedes Architecture Forum Exhibition called Living Prototypes
<p>The presented Dataset is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The collected data is part of Indoor Monitoring Framework sensed data (temperature and humidity) stored in InfluxDB in CSV format for Living Prototype Exhibition at Aedes Forum in Berlin for a period of 35 days. The dataset can be used in InfluxDB and Grafana for visualization. </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.