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309 results for “multidimensional”
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Material Complementario: Medición de la pobreza energética con enfoque multidimensional: revisión sistemática de la literatura
<p><strong>Material Complementario</strong></p><p><i>"Medición de la pobreza energética con enfoque multidimensional: revisión sistemática de la literatura"</i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores los siguientes documentos y materiales complementarios:</p><ol><li>Scopus_Searches.bib</li><li>Lista_de_Documentos_Identificados.xlsx</li><li>Ind_Mult_PE.pdf</li></ol><p>En el primer archivo, podrán encontrar los datos en crudo de la búsqueda realizada en la base de datos Scopus; seguidamente, en la planilla de Lista de Documentos, podrán encontrar de manera detallada la lista de materiales bibliográficos utilizados en el marco del estudio y por último en el documento en PDF "Ind_Mult_PE" podrán encontrar una lista detallada de indicadores y métricas multidimensionales de pobreza energética que fueron identificados en el marco de la revisión de la litera, inclusive algunos que no fueron incluidos en el artículo. </p><p>Estos materiales de apoyo fueron puestos a disposición de todos los interesados para fines académicos y científicos. </p>
Dataset: Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay
<p><i><strong>"Analysis of Multidimensional Energy Poverty in the Carmen Soler Community - Limpio, Republic of Paraguay"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el cálculo del Índice de Pobreza Energética Multidimensional (MEPI) para el caso de estudio. </p><ol><li>MEPI_CarmenSoler_Data_2018_CHILECON2023.xlsx</li></ol><p>En el archivo, podrán encontrar los extraídos de los resultados de la encuesta realizada en el 2018 por un equipo de investigadores paraguayos (En el artículo podrán encontrar más información). Además de los datos, podrán ver todos los pasos y cálculos llevados a cabo para obtener los resultados obtenidos. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Caracterización multidimensional de la vivienda tradicional
<p><strong>Coordinador del Seminario</strong>: Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Luis Adolfo Ortega Granados</p> <p><strong>Comité Ejecutivo PRONACE-Vivienda</strong><br> Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).<br> Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.<br> Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).<br> Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> Lárraga, R. (2014). Caracterización multidimensional de la vivienda tradicional en la Huasteca Potosina. Revista caribeña de Ciencias Sociales (2014_08). <br> https://econpapers.repec.org/article/ervrccsrc/y_3a2014_3ai_3a2014_5f08_3a09.htm</p>
Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)
<p>This dataset comprises historical information about road accidents in Colombia from 2017 to 2021, titled 'Road Accidents 2017-2021', containing 18,600 records of accident events on roads managed by the National Roads Institute (INVÍAS, 2021). The dataset includes 41 descriptors and was last updated on July 15, 2022. It has been published under the Open Data initiative (Law 1712 of 2014 on Transparency and Access to National Public Information).</p> <p>In addition to accident information, the dataset integrates a database with holiday dates and road identifiers, ensuring data coherence and quality for data analysis purposes. Statistical analysis is conducted through exploratory data analysis focusing on the years 2017 to 2021, utilizing Python (version 3.10) within the Jupyter Notebooks execution environment and specialized libraries (Pandas, NumPy, Matplotlib, and Seaborn), due to their ease of application for this dataset. After data normalization, the dataset comprises 18,554 records, with 46 excluded due to inconsistent data formats.</p>
Sanitation Poverty: a multidimensional measure
<p>This file provides the primary data (database file, in *csv) and achieved results (results file, in *csv) published in the paper "Monitoring and Targeting the Sanitation Poor: A Multidimensional Approach", Natural Resources Forum, 2018 (under review).</p> <p>The paper discusses the adequacy and applicability of one approach that is increasingly adopted for multidimensional poverty measurement at the household level, the Alkire-Foster methodology. Drawing on this method, we identify and combine a set of direct household-related water and sanitation deprivations that a person experiences at the same time. This new multidimensional measure is useful for gaining a better understanding of the context in which WaSH services are delivered. It captures both the incidence and intensity of WaSH poverty, and provides a new tool to support monitoring and reporting, as well as targeting and planning. For illustrative purposes, one small town in Mozambique is selected as the initial case study.<br> </p>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Multidimensional pain profiling in people living with obesity and attending weight management services: a protocol for a longitudinal cohort study
<p><strong>Please note: The final dataset will not be available until data collection has been completed in the Autumn of 2024.<br> This dataset currently contains the following:</strong></p> <p>1. Details of the study, including authorship, ethical approval, funding, registration details, an abstract for the protocol of the study and details of data being collected (both in Microsoft Word and open access .txt formats)</p> <p>2. Outline of the data in the process of collection (Microsoft Excel)</p> <p>3. Ethical approval letters from relevant Research Ethics Committees (PDF)<br> <br> </p> <p> </p> <p><strong>Project Title: </strong>Multidimensional pain profiling in people living with obesity and attending weight management services: a longitudinal cohort study</p> <p> </p> <p><strong>Authors:</strong> Keith M. Smart<sup>1,2</sup>, Natasha Hinwood<sup>1</sup>, Colin G. Dunlevy<sup>3</sup>, Catherine Doody<sup>1</sup>, Catherine Blake<sup>1</sup>, Brona Fullen<sup>1</sup>, Jean O’Connell<sup>3</sup>, Carel W. Le Roux<sup>4</sup>, Clare Gilsenan<sup>5</sup>, Francis M. Finucane<sup>6,7</sup>, Gráinne O’Donoghue<sup>1</sup>.</p> <p> </p> <p><strong>Corresponding author</strong>: Natasha Hinwood</p> <p><strong>Address:</strong> UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><strong>Email:</strong> <a href="mailto:natasha.hinwood@ucdconnect.ie">natasha.hinwood@ucdconnect.ie</a></p> <p><strong>Phone:</strong> +353 1 716 6511</p> <p> </p> <p>Full name, department, institution, city, and country of all co-authors.</p> <p><sup>1</sup>UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><sup>2</sup>Physiotherapy Department, St. Vincent’s University Hospital, Dublin, Ireland</p> <p><sup>3</sup>Weight Management Service, St Columcille’s Hospital, Dublin, Ireland</p> <p><sup>4</sup>Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland</p> <p><sup>5</sup>Physiotherapy Department, Beaumont Hospital, Dublin, Ireland</p> <p><sup>6</sup> School of Medicine, College of Nursing and Health Sciences, University of Galway</p> <p><sup>7</sup>Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals</p> <p> </p> <p><strong>ORCID</strong></p> <p>1. Keith M. Smart: 0000-0002-1598-5215</p> <p>2. Natasha Hinwood: 0000-0001-9382-716X</p> <p>3. Colin G. Dunlevy:</p> <p>4. Catherine Doody:</p> <p>5. Catherine Blake: 0000-0002-0600-629X</p> <p>6. Brona Fullen: 0000-0003-4408--2063</p> <p>7. Carel W. Le Roux: 0000-0001-5521-5445</p> <p>8. Jean O’Connell: 0000-0001-7241-8025</p> <p>9. Clare Gilsenan:</p> <p>10. Francis M. Finucane: 0000-0002-5374-7090</p> <p>11. Gráinne O’Donoghue: 0000-0002-9126-2094</p> <p> </p> <p> </p> <p><strong>Project abstract (Protocol): </strong></p> <p><em>Introduction</em>:</p> <p>Pain is prevalent in people living with overweight and obesity. Obesity is associated with increased self-reported pain intensity and pain-related disability, reductions in physical functioning and poorer psychological well-being. People living with obesity tend to respond less well to pain treatments or management compared to people living without obesity. Mechanisms linking obesity and pain are complex and may variously include contributions from and interactions between physiological, behavioural, psychological, socio-cultural, biomechanical, and genetic factors. Our aim is to study the multidimensional pain profiles of people living with obesity, over time, in an attempt to better understand the relationship between obesity and pain.<br> </p> <p><em>Methods and analysis: </em></p> <p>This longitudinal observational cohort study will recruit (n=216) people living with obesity and who are newly attending three weight management services in Ireland. Participants will complete questionnaires that assess their multidimensional biopsychosocial pain experience at baseline and at 3, 6, 12 and 18-months post-recruitment. Quantitative analyses will characterise the multidimensional pain experiences and trajectories of the cohort as a whole and in defined sub-groups.<br> </p> <p><em>Ethics and dissemination: </em></p> <p>The study protocol has been approved by the Ethics and Medical Research Committee of St Vincent’s Healthcare Group, Dublin, Ireland (Reference No.: RS21-059) and the University College Dublin Human Research Ethics Committee (Reference No.: LS-E-22-41-Hinwood-Smart). Findings will be disseminated through peer-reviewed journals, conference presentations, public and patient advocacy groups, and social media.</p>
Traditional Chinese Medicine Multidimensional Knowledge Graph
<h3>Overview of the Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</h3> <p>The <strong>Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</strong> is a comprehensive, open-source data platform developed by Jingqi Zeng in November 2024. This platform aims to integrate and standardize a vast array of data from multiple sources, encompassing both traditional Chinese medicine (TCM) and modern biomedical sciences. By organizing and linking this diverse information, TCM-MKG acts as a bridge that connects the ancient wisdom of TCM with contemporary medical research and applications.</p> <h3>Key Features and Objectives:</h3> <ul> <li> <p><strong>Multi-source Data Integration</strong>: TCM-MKG consolidates data from over 30 authoritative resources, covering a broad spectrum of topics, including TCM terminology, Chinese patent medicines (CPM), Chinese herbal pieces (CHP), natural products (NP), chemical components, disease targets, and more. These data sources are carefully curated and interlinked, ensuring a rich, multi-dimensional view of TCM in relation to modern biomedical research. The platform incorporates data from reputable databases such as DrugBank, BioGRID, DisGeNET, STRING, and many others, ensuring that the TCM knowledge is not only expansive but also scientifically robust and cross-referenced with global biomedical standards.</p> </li> <li> <p><strong>Standardized Design for Global Interoperability</strong>: TCM-MKG adheres to international data standards and integrates with widely-used global medical classification systems such as ICD-11, UMLS, MeSH, and DOID. This ensures that the platform’s data is globally comparable and facilitates easy integration with international research efforts, promoting collaboration and knowledge exchange across the fields of TCM and modern medicine.</p> </li> <li> <p><strong>Open Source and Collaborative</strong>: In line with its mission to enhance transparency and accessibility, TCM-MKG is open-sourced in a structured tabular format. This allows researchers worldwide to freely access, contribute to, and expand upon the data, fostering interdisciplinary collaboration and accelerating innovation in both TCM research and modern medicine.</p> </li> <li> <p><strong>Advanced Analytical Capabilities</strong>: By leveraging the power of knowledge graph technology and graph-based intelligence algorithms, TCM-MKG supports deep data mining and relational reasoning. Researchers can uncover hidden associations between TCM components, diseases, and targets, providing insights into the mechanisms of herbal interactions and offering new pathways for drug discovery and therapeutic research.</p> </li> </ul> <h3>Personal Research Application:</h3> <p>Using the TCM-MKG platform, I conducted a study titled <strong>"Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine."</strong> This research applied advanced graph intelligence algorithms to quantitatively assess the compatibility mechanisms of Chinese herbal formulas (CHF). The study provides fresh insights into the underlying principles of TCM herbal combinations.</p> <p>This research has been published:</p> <p><strong>Zeng, J., & Jia, X. (2025). Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks. <em>Journal of Pharmaceutical Analysis</em>, 101342. <a href="https://doi.org/10.1016/j.jpha.2025.101342">https://doi.org/10.1016/j.jpha.2025.101342</a></strong></p> <p>The code and methodology for this research have been open-sourced and are available on <a href="https://github.com/ZENGJingqi/GraphAI-for-TCM" target="_new" rel="noopener">GitHub</a>.</p> <h3>Acknowledgments:</h3> <p>This work benefited from the integration of data from numerous open-access and authoritative databases. We acknowledge the valuable contributions of resources such as DrugBank, BindingDB, BioGRID, DisGeNET, and many others. These datasets provided essential insights into TCM, modern drug chemistry, genetics, diseases, and related fields, forming the foundation for the traditional Chinese medicine multi-dimensional knowledge graph (TCM-MKG) used in this study. Furthermore, we utilized the PSICHIC model (https://github. com/huankoh/PSICHIC) to analyze the binding interactions between components and targets. Full citations for these resources are included.</p> <h3>Contact Information:</h3> <p>For further inquiries or more detailed information, please feel free to contact:<br><strong>Email</strong>: <a rel="noopener">zjingqi@163.com</a></p> <p> </p>
Pobreza multidimensional como limitante del desarrollo humano en la comunidad 28 de Marzo de Santa Inés, estado Lara, Venezuela
<p>La presente investigación describe los niveles de pobreza multidimensional y sus implicaciones para el desarrollo humano en los hogares del sector 28 de Marzo de la comunidad de Santa Inés, municipio Urdaneta del estado Lara durante el primer semestre de 2022. El estudio se realizó desde el paradigma cuantitativo,<br>fundamentándose en la metodología de medición de pobreza multidimensional de Alkire y Foster y algunas teorías del desarrollo humano. Ubicándose en la línea de investigación Sustentabilidad, equidad y participación del programa de Desarrollo Humano del Decanato Experimental de Humanidades y Artes de la Universidad<br>Centroccidental Lisandro Alvarado. La población estuvo compuesta por 105 viviendas del sector 28 de Marzo y la muestra fue de 87 viviendas. El instrumento fue un cuestionario de 18 ítems y se calculó la confiabilidad en 95%. Como técnica de análisis de datos se utilizó la estadística descriptiva a través de la distribución de frecuencia. Entre los resultados obtenidos, desde las dimensiones abordadas de salud, trabajo, educación, condiciones de la niñez y juventud, se evidenció que existe un nivel alto de privaciones relacionadas con la pobreza multidimensional como las precarias condiciones en los servicios de salud, aseo urbano, agua y<br>telefonía móvil, que inciden en el desarrollo adecuado de sus capacidades</p>
dataset for "Non-LTE Synthetic Observables of a Multidimensional Model of Type Ia Supernovae"
<p>Included here are datafiles supplementary to the article "Non-LTE Synthetic Observables of a<br>Multidimensional Model of Type Ia Supernovae" (Boos, Dessart, Townsley, Shen), submitted <br>to ApJ/arxiv in October 2024. This is a revised version of the dataset using an improved<br>method to generate the 1D LTE observables (1D LTE and pseudo-2D non-LTE observables have<br>thus changed from the previous version).</p> <p>We include in this dataset the 2D double detonation ejecta model (produced in Boos et al. 2021),<br>as well as the 15 wedge profiles constructed from this model, which were used in the radiative<br>transfer calculations of this work. These profiles are provided in Sedona input format. The<br>headers/columns for the 1D profiles are as follows:<br>1: Sedona model type<br>2: num_cells, rmin, ejecta time, num_isos<br>3: isos<br>4-: v_r, rho, T, X_a, X_b, etc.</p> <p>We also include the synthetic spectra and photometry from this work. These synthetic observables<br>are given for each set of calculations (1D LTE, 2D LTE, 1D non-LTE, and pseudo-2D non-LTE) between<br> -3 and +15 d from maximum light. The photometry is given in absolute magnitude in the Vega system.<br>The spectra have been rebinned such that the bins are the same between the Sedona and CMFGEN<br>calculations. The flux in these spectral files is given for a distance of 1 kpc.</p> <p><br>-Samuel J. Boos (sjboos@crimson.ua.edu)</p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Multidimensional impact assessment of a large collection of books using PlumX: methodology, technical limitations and indicators analysis [Complementary material to manuscript]
<p>The main purpose of this macro-study is to shed light on the broad impact of books. For this purpose, the impact a very large collection of books (more than 200,000) has been analysed by using PlumX, an analytical tool providing a great number of different metrics provided by various tools. Furthermore, the study focuses on the evolution of the most significant measures and indicators over time. The results show usage counts in comparison to the other metrics are quantitatively predominant. Catalogue holdings and reviews represent a book’s most characteristic measures deriving from its increased level of impact in relation to prior results. Our results also corroborate the long half-life of books within the scope of all metrics, excluding views and social media. Despite of some disadvantages, PlumX has proved to be a very helpful and promising tool for assessing the broad impact of books, especially because of how easy it is to enter the ISBN directly as well as its algorithm to aggregate all the data generated by the different ISBN variations.</p>
Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi
<p>Premise of the study While many studies have measured the aboveground responses of plants to mycorrhizal fungi at a single time point, little is known about how plants respond belowground or across time to mycorrhizal symbiosis. By measuring belowground responses as well as growth over time in many plant species, we create a more complete picture of how mycorrhizal fungi benefit their hosts. Methods We grew 26 prairie plant species with and without mycorrhizal fungi and measured fourteen functional traits measuring above and belowground tissue quality and quantity responses and changes in resource allocation. We used function-value trait (FVT) modeling to characterize changes in species growth rate when colonized. Key results While aboveground biomass responses were positive, the response of traits belowground were much more variable. Changes in aboveground biomass accounted for 60.8% of the variation in mycorrhizal responses, supporting the use of aboveground biomass response as the primary response trait. Responses belowground were not associated with aboveground responses and accounted for 18.3% of the variation. Growth responses over time were highly variable across species. Interestingly, none of the measured responses were phylogenetically conserved. Conclusions Mycorrhizal fungi increase plant growth in most scenarios, but the effects of these fungi belowground and across time are more complicated. This study highlights how differences in plant allocation priorities might affect how they utilize the benefits from mycorrhizal fungi. Identifying and characterizing these differences is a key step to understanding the effects of mycorrhizal mutualisms on whole plant physiology. </p>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
Code4Bench: A Multidimensional Benchmark of Codeforces Data for Different Program Analysis Techniques
<p>Reproducible research relies on well-designed benchmarks. However, evaluation on a single benchmark increases the risk of overfitting; that is, an optimization to reach a certain performance. In recent years several well-designed benchmarks have been constructed for different subfields of program analysis. However, they often involve real-world industrial projects in few languages such as C or Java. We provide Code4Bench, a benchmark comprising 3,421,357 programs totaling of 306,053,105 lines of code in 41 versions of 28 programming languages such as C/C++, Java, Python, and Kotlin. We have constructed this benchmark from Codeforces, a famous programming competition website, which is widely used by international programmers. Code4Bench advances the state-of-the-art in conducting reproducible and comparative experiments. It helps mitigate the bias and increase the generality and conclusiveness of the results. We present our methodology in construction of Code4Bench and give various descriptive statistics. We have also conducted an online survey on the users of Codeforces’ website whose code is included in the benchmark. The survey is concerned about the user’s demographic information and programming habits, whose results are also provided in the benchmark. Finally, we leveraged an automatic process by which we localized faults within the faulty versions and categorize them according to a coarse-grained classification. In addition to its usage in empirical studies, Code4Bench can be used to teach programming and evolve algorithmic problems. We release Code4Bench in database format to allow researchers to extract other data of the benchmark by arbitrary queries.</p> <p>Code4Bench version 1.0.0 is publicly available at <a href="https://zenodo.org/record/2582968">https://zenodo.org/record/2582968</a>, with DOI 10.5281/zenodo.2582968, thereby providing long-term storage and versioning. It is released under the terms of Creative Commons Attribution 4.0 International license. Code4Bench is also publicly available at: <a href="https://github.com/code4bench/Code4Bench">https://github.com/code4bench/Code4Bench</a>, in which we have provided some additional information and script examples.</p>
Fig. 3. Non-metric multidimensional scaling analysis for the 13 in Trophic structure of a fish community in Bananal stream subbasin in Brasília National Park, Cerrado biome (Brazilian Savanna), DF
Fig. 3. Non-metric multidimensional scaling analysis for the 13 more abundant fish species' diet found in the Bananal stream subbasin, Paranoá Lake basin, DF. Indication of four groups A, B, C and D. Species abbreviations: Aspidoras fuscoguttatus (aspfus), Astyanax sp. (astsp), Characidium xanthopterum (chaxan), Hasemania sp. (hassp), Hyphessobrycon balbus (hypbal), Heptapterus sp. (hepsp), Knodus moenkhausii (knomoe), Kolpotocheirodon theloura (kolthe), Moenkhausia sp. (moesp), Phalloceros harpagos (phahar), Planaltina myersi (plamye), Rhamdia quelen (rhaque) and Rivulus pictus (rivpic).
Light limitation increases multidimensional trait evenness in phytoplankton populations
<p>This archive includes the final table used for the analyses and a readme file with some explanations.</p> <p>Our dataset consists of phytoplankton cell density and trait diversity estimates measured using scanning flow cytometry under different light intensity regimes.</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.