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45 results for “resource assessment”
Data from: Development of a simple, practice-based tool to assess quality of paediatric emergency care delivery in resource-limited settings: identifying critical actions via a Delphi study
Objective: Provision of timely, high-quality care for the initial management of critically ill children in African hospitals remains a challenge. Monitoring the completion of critical actions during resuscitations can inform efforts to reduce variability and improve outcomes. We sought to develop a practice-based tool based on contextually relevant actions identified via a Delphi process. Our goal was to develop a tool that could identify gaps in care, facilitate identification of training and standardized assessment to support quality improvement efforts. Design: Six sentinel conditions were selected based on disease epidemiology and mortality at rural and urban African emergency departments. Potential critical actions were identified through focused literature review. These actions were evaluated within a three-round modified Delphi process. A set of logistical filters was applied to the candidate list to derive a practice-based tool. Setting and participants: Attendees at an international emergency medicine conference comprised an expert panel of 25 participants, with 84% working primarily in African settings. Consensus rounds allowing novel responses were conducted via online and in-person surveys. Results: The expert panel generated 199 actions that apply to six conditions in emergently ill children. Application of appropriateness criteria refined this to 92 candidate actions across seven categories: core skills, active seizure, altered mental status, diarrheal illness, febrile illness, respiratory distress, polytrauma. From these, we identified 28 actions for inclusion in a practice-based tool contextually relevant to the initial management of critically ill children in Africa. Conclusions: A group consensus process identified critical actions for severely ill children with select sentinel conditions in emergency paediatric care in an African setting. Absence of these actions during resuscitation might reflect modifiable gaps in quality of care. The resulting practice-based tool is context-relevant and can serve as a foundation for training and quality improvement efforts in African hospitals and emergency departments.
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
Mapping and Quantifying Mexico's Primary Forests: An Operational Approach for Forest Resources Assessment
<h2>Supplementary material of the paper</h2> <h3>Authors:</h3> <ul> <li>Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</li> <li>Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla</li> </ul> <h3>More information:</h3> <ul> <li>lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</li> <li>mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</li> </ul> <p>Supplementary material containing the vector layers with the information on the exclusion areas and the forest resulting from the multicriteria analysis for the identification of primary forests in Mexico for the years 2015 and 2020.</p> <h3>Summary:</h3> <p>The file is organized as follows:</p> <p>> Primary forest MX</p> <p> > 2015</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000masl (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2015 (Bosque_primario_2015.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2015_1km (Deforestacion2015_1km.shp)</p> <p> - AgriculturalBorder_SII_1km (FronteraAgricola_SII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - redvial2015_1km (redvial2015_1km.shp)</p> <p> > 2020</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000msnm (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2020 (Bosque_primario_2020.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2020_1km (Deforestacion2020_1km.shp)</p> <p> - Agricultural_Front_SIII_1km (FronteraAgricola_SIII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - road_network2020_1km (redvial2020_1km.shp)</p> <p>*All shape files are accompanied by their respective auxiliary files.</p> <p>Supplementary material 1-4.docx: Word file containing the supplementary materials named in the article, in the following order:</p> <ul> <li>Supplementary material 1. Decision tree.</li> <li>Supplementary material 2. Data sets.</li> <li>Supplementary material 3. Primary forest area by ecoregion and forest type for 2015 and 2020</li> <li>Supplementary material 4. Statistics on concordance and ecological integrity values in primary and other forests in 2020.</li> </ul> <h2>Material suplementario del artículo </h2> <h3>Autores:</h3> <p>- Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</p> <p>- Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla </p> <h3>Más información:</h3> <p>- lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</p> <p>- mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</p> <p><strong>Bosque primario MX.7z: </strong>Material suplementario que contiene las capas vectoriales con las información de las áreas de exclusión y el bosque resultante del análisis multicriterio para la identificación de bosques primarios en México para los años 2015 y 2020. </p> <h3>Resumen:</h3> <p>El archivo se encuentra organizado de la siguiente forma:</p> <p>> Bosque primario MX</p> <p> > 2015</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2015.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2015_1km.shp</p> <p> - FronteraAgricola_SII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2015_1km.shp</p> <p> > 2020</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2020.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2020_1km.shp</p> <p> - FronteraAgricola_SIII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2020_1km.shp</p> <p>*Todos los archivos shape estan acompomañados de sus respectivos archivos auxiliares.</p> <p>Supplementary material 1-4.docx: Archivo Word que contiene los materiales suplementarios nombrados en el articulo, en el siguiente orden:</p> <ul> <li>Material complementario 1. Árbol de decisiones.</li> <li>Material complementario 2. Conjuntos de datos.</li> <li>Material complementario 3. Área de bosque primario por ecorregión y tipo de bosque para 2015 y 2020</li> <li>Material complementario 4. Estadísticas sobre valores de concordancia e integridad ecológica en bosques primarios y otros bosques en 2020.</li> </ul>
Comparison Metrics Microscale Simulation Challenge for Wind Resource Assessment - Perdigão
<p>Simulation results of the "Comparison Metrics Microscale Simulation Challenge for Wind Resource Assessment". Simulations with various models and tools were performed of the Perdigão site. The wind speed profiles for nine metmast positions and the AEP values for two metmast positions are made available. At each position the wind speed profiles and AEP values are given for twelve wind direction sectors.</p>
Data set and analytic codes supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia"
<p>This deposit contains data set and analytic codes (with a meta data) supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia".</p> <p>We investigated how habitat structure and complexity within smallholding oil palm plantations affected resource-use behaviours of two common butterfly species in the study areas (oil palm plantations in Banting, Selangor, Malaysia): Leptosia nina (Pieridae) and Ypthima spp. (Nymphalidae). Using direct-observation methods we developed, we followed seven and nine individuals of each species respectively, for three minutes in smallholder-owned immature monoculture and polyculture oil palm, and mature monoculture oil palm plantations. We recorded distance travelled by each individual from both straight line and the sums of distances between all perching points, and the position and characteristics of each perch location, where the individual landed. We compared our observations to control runs, generated by pairing observed distances travelled, but selecting the direction for each movement at random. By comparing the distance travelled and characteristics of locations used by butterflies and paired control points across habitats, we assessed how individuals in the two species used the local environment and whether this differed with habitat structure and complexity.</p> <p>Funding and research permission: Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund funded MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) funded the establishment of the plots and surveys of environmental parameters. Research permission was granted by the Economic Planning Unit (EPU) of Malaysia’s Prime Minister’s Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
Compliance to Cervical Cancer Chemoradiation Guidelines: A Multicentric Implementation Audit and Resource Assessment Initiative of National Cancer Grid of India
ClinicalTrials.gov study NCT07290972. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Study to Assess Resource Utilization and Quality of Life of Patients With RRMS Treated With Tecfidera in Greece
ClinicalTrials.gov study NCT03101735. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
GRACE: Geriatric Resources for Assessment and Care of Elders
ClinicalTrials.gov study NCT00182962. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Data from: Assessing the spatial ecology and resource use of a mobile and endangered species in an urbanized landscape using satellite telemetry and DNA faecal metabarcoding
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Data from: Development of a simple, practice-based tool to assess quality of paediatric emergency care delivery in resource-limited settings: identifying critical actions via a Delphi study
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Data from: Noise affects resource assessment in an invertebrate
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ASSESSMENT OF RESOURCE COMPETENCE IN THE DEVELOPMENT OF AGRO-TOURISM ACTIVITIES IN FERGANA PROVINCE
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Data from: The condition dependency of fitness in males and females: the fitness consequences of juvenile diet assessed in environments differing in key adult resources
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Data from: "Identification and assessment of single nucleotide polymorphisms (SNPs) between Culex complex mosquitoes." in Genomic Resources Notes Accepted 1 August 2014-30 September 2014
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Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California: Data and Software
<p>These files contain the raw data and code used to analyzed wind resource and local load compatibility of offshore wind in Humboldt, California.</p>
Retrospective Assessment of AE-Related Healthcare Resource Utilization and Costs of Immune Checkpoint Inhibitor and Targeted Therapy for Adjuvant Treatment of Melanoma
ClinicalTrials.gov study NCT05874817. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing the Efficiency of Cognitive Group Therapy for Patient With Subjective Memory Complains Using the WebNeuro System From Brain Resource(BRC)
ClinicalTrials.gov study NCT01913626. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Retrospective Assessment of Adverse Events-related Healthcare Resource Utilization and Costs of Immune Checkpoint Inhibitor and Targeted Therapy for Adjuvant Treatment of Melanoma
ClinicalTrials.gov study NCT05714371. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of the Psychological, Cognitive and Social Resources of Applicants for Huntington's Disease and Presymptomatic Genetic Testing
ClinicalTrials.gov study NCT02134561. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Assess Treatment Patterns, Clinical Outcomes, and Healthcare Resource Utilization in Chinese Participants Receiving Upadacitinib for Atopic Dermatitis (AD) Through Chart Review
ClinicalTrials.gov study NCT06503536. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.