cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.21996761
title: Supplemental information files for Are classical fibre composite models appropriate for material extrusion additive manufacturing? A thorough evaluation of analytical models

description: Supplementary files for article Are classical fibre composite models appropriate for material extrusion additive manufacturing? A thorough evaluation of analytical models <br> To improve mechanical properties, fibre reinforcement has been used in material extrusion additive manufacturing (MEAM). However, there are few reports about fibre composite mechanics and property prediction in MEAM. This study evaluated seventeen classical composite numerical models to assess their prediction capabilities and applicability in MEAM. Four carbon fibre reinforced polymer composites (PLA, ABS, PA, and PETG) were used. Longitudinal strength and modulus were predicted via the rule of mixtures (RoM), modified rule of mixtures (MRoM), Kelly model, Cox shear lag model, and modified Cox models. Transverse strength was predicted via the RoM transverse model and bridging model. Transverse modulus was predicted via the RoM transverse model, Halpin-Tsai, and modified Halpin-Tsai models. These models are widely accepted in the composite field but have not been assessed for MEAM. The predicted strengths and moduli were normalised by experimental values. For all materials, all models overestimated the longitudinal strength. The best predictions were found for the Kelly model, Cox-Krenchel model, and modified Cox model (with porosity) for longitudinal strength: normalised strengths were 2.02, 1.54, 1.63, respectively, averaged for all materials, compared to 2.46–8.82 for other models. Longitudinal modulus was well-predicted by the Kelly model and Cox-based models (normalised modulus of 0.87–2.05). The RoM transverse model and bridging model accurately predicted transverse modulus and strength, respectively (normalised strength 0.99 and normalised modulus 1.14). Additionally, model efficiency also varied between materials. PA was more predictable for longitudinal strength than PLA, PETG, and ABS (normalised strengths: 3.19, 3.98, 4.56, and 4.81, respectively) and in modulus (normalised moduli: 2.64, 3.01, 2.84, and 2.99), but less predictable in transverse strength (1.74, 2.02, 1.29, and 1.64) and transverse modulus (1.90, 2.02, 0.92, and 1.13). Fractography evidenced non-uniform fibre length, non-uniform fibre orientation, and weak fibre-matrix interface, which likely caused the discrepancies between theoretical and experimental properties. Potential revisions to improve model accuracy were discussed. Factors including fibre orientation, fibre length, fibre clustering, porosity, and fibre-matrix interface were highlighted for better prediction, which facilitates better understanding in mechanics and modelling of 3D printing fibre composites.
DOI: 10.17028/rd.lboro.21996761.v1
title: Supplemental information files for Are classical fibre composite models appropriate for material extrusion additive manufacturing? A thorough evaluation of analytical models

description: Supplementary files for article Are classical fibre composite models appropriate for material extrusion additive manufacturing? A thorough evaluation of analytical models <br> To improve mechanical properties, fibre reinforcement has been used in material extrusion additive manufacturing (MEAM). However, there are few reports about fibre composite mechanics and property prediction in MEAM. This study evaluated seventeen classical composite numerical models to assess their prediction capabilities and applicability in MEAM. Four carbon fibre reinforced polymer composites (PLA, ABS, PA, and PETG) were used. Longitudinal strength and modulus were predicted via the rule of mixtures (RoM), modified rule of mixtures (MRoM), Kelly model, Cox shear lag model, and modified Cox models. Transverse strength was predicted via the RoM transverse model and bridging model. Transverse modulus was predicted via the RoM transverse model, Halpin-Tsai, and modified Halpin-Tsai models. These models are widely accepted in the composite field but have not been assessed for MEAM. The predicted strengths and moduli were normalised by experimental values. For all materials, all models overestimated the longitudinal strength. The best predictions were found for the Kelly model, Cox-Krenchel model, and modified Cox model (with porosity) for longitudinal strength: normalised strengths were 2.02, 1.54, 1.63, respectively, averaged for all materials, compared to 2.46–8.82 for other models. Longitudinal modulus was well-predicted by the Kelly model and Cox-based models (normalised modulus of 0.87–2.05). The RoM transverse model and bridging model accurately predicted transverse modulus and strength, respectively (normalised strength 0.99 and normalised modulus 1.14). Additionally, model efficiency also varied between materials. PA was more predictable for longitudinal strength than PLA, PETG, and ABS (normalised strengths: 3.19, 3.98, 4.56, and 4.81, respectively) and in modulus (normalised moduli: 2.64, 3.01, 2.84, and 2.99), but less predictable in transverse strength (1.74, 2.02, 1.29, and 1.64) and transverse modulus (1.90, 2.02, 0.92, and 1.13). Fractography evidenced non-uniform fibre length, non-uniform fibre orientation, and weak fibre-matrix interface, which likely caused the discrepancies between theoretical and experimental properties. Potential revisions to improve model accuracy were discussed. Factors including fibre orientation, fibre length, fibre clustering, porosity, and fibre-matrix interface were highlighted for better prediction, which facilitates better understanding in mechanics and modelling of 3D printing fibre composites.
DOI: 10.6084/m9.figshare.21996764
title: Effects of vitamin E and selenium administration on transportation stress in pregnant dairy heifers - supplementary material

description: Ingredient and chemical composition of feed and circulating ESe results in this study
DOI: 10.5287/bodleian:2aykqqdaz
title: BeeHIVE: Behavioral Biometric System based on Object Interactions in Smart Environments

description: This is the dataset collected for the paper "BeeHIVE: Behavioral Biometric System based on Object Interactions in Smart Environments". We provide a .zip file with the collected data and a readme file that describes the data format and structure. If you use any of the data, please cite the original paper as follows: @article{krawiecka2022beehive, author = {Krawiecka, Klaudia and Birnbach, Simon and Eberz, Simon and Martinovic, Ivan}, title = {BeeHIVE: Behavioral Biometric System based on Object Interactions in Smart Environments}, year = {2022}, url = {https://arxiv.org/abs/2202.03845}, eprint = {2202.03845}, archivePrefix = {arXiv}, primaryClass = {cs.CR} }
DOI: 10.3886/e183663
title: Replication data and code for Monetary Policy and Inequality

description: These replication files use data from the Survey of Consumer Finances to compute household-level exposures to changes in monetary policy. The replication file also estimates the causal effect of monetary policy on a list of macroeconomic variables.<br>
DOI: 10.3886/e183663v1
title: Replication data and code for Monetary Policy and Inequality

description: These replication files use data from the Survey of Consumer Finances to compute household-level exposures to changes in monetary policy. The replication file also estimates the causal effect of monetary policy on a list of macroeconomic variables.<br>
DOI: 10.35078/yqiury
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Teófilo Otoni | Município: Carlos Chagas

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.6084/m9.figshare.14992134
title: Development and pilot-testing of a behavioural intervention to enhance physical activity in patients admitted to the cardiology ward: a proof-of-concept study

description: To examine the feasibility and potential effectiveness of a behavioural intervention to increase physical activity in patients admitted to the cardiology ward. In this proof-of concept study, we developed an intervention supporting self-efficacy and diminishing inertia by means of autonomy support and action planning. Data collection took place from April till July 2018, including a three-week intervention period. Feasibility was assessed with a semi-structured interview among patients (<i>n</i> = 34), a survey among nurses (<i>n</i> = 20), and a focus group among physical therapists (<i>n</i> = 4). Potential effectiveness was assessed as change in action planning, autonomy support and self-efficacy (determined using psychological questionnaires) in 30 patients, and change in physical activity (determined using behavioural mapping) before (<i>n</i> = 90) and after (<i>n</i> = 83) implementation. Feasibility of the behavioural intervention was deemed adequate by patients, nurses and physical therapists. Potential effectiveness was demonstrated by an increase in action planning (mean difference: 1.76; 95%-CI: 0.92–2.60), autonomy support (0.84; 0.35–1.34) and self-efficacy (0.57; 0.11–1.03). Patients in the after-group spent less time lying down (-5.96; −8.86 to −2.86) and more time standing/walking (3.34; 1.82–4.87). The developed behavioural intervention appears to be feasible in daily clinical practice and may enhance relevant psychological determinants and physical activity in patients admitted to the cardiology ward.
DOI: 10.6084/m9.figshare.14992134.v1
title: Development and pilot-testing of a behavioural intervention to enhance physical activity in patients admitted to the cardiology ward: a proof-of-concept study

description: To examine the feasibility and potential effectiveness of a behavioural intervention to increase physical activity in patients admitted to the cardiology ward. In this proof-of concept study, we developed an intervention supporting self-efficacy and diminishing inertia by means of autonomy support and action planning. Data collection took place from April till July 2018, including a three-week intervention period. Feasibility was assessed with a semi-structured interview among patients (<i>n</i> = 34), a survey among nurses (<i>n</i> = 20), and a focus group among physical therapists (<i>n</i> = 4). Potential effectiveness was assessed as change in action planning, autonomy support and self-efficacy (determined using psychological questionnaires) in 30 patients, and change in physical activity (determined using behavioural mapping) before (<i>n</i> = 90) and after (<i>n</i> = 83) implementation. Feasibility of the behavioural intervention was deemed adequate by patients, nurses and physical therapists. Potential effectiveness was demonstrated by an increase in action planning (mean difference: 1.76; 95%-CI: 0.92–2.60), autonomy support (0.84; 0.35–1.34) and self-efficacy (0.57; 0.11–1.03). Patients in the after-group spent less time lying down (-5.96; −8.86 to −2.86) and more time standing/walking (3.34; 1.82–4.87). The developed behavioural intervention appears to be feasible in daily clinical practice and may enhance relevant psychological determinants and physical activity in patients admitted to the cardiology ward.
DOI: 10.48323/pcra0-xqk09
title: MS_Lumos_2023_January
DOI: 10.17031/63dbc6964ea25
title: Diatoms counted in CPR data from 1958-2020

description: Excel file containing diatom counts from CPR data from the North Atlantic (45N to 65N -17W to 7E) between 1958 and 2020, sample level data (each sample being 1 row, equating to approx. 10 nautical miles of tow, 3m3 of filtered seawater). There are two excel sheets, the first contains the taxa abundance counts, the second contains the taxa names and if that name includes group data (1=grouped). For each taxa, values indicate abundance (according to standard CPR methodology, see Richardson et al. 2006, Prog in Oceanog 68. 27-74), zero values are absence of taxon, empty cells are when the taxon was not looked for. The excel file consists of samples in rows, with the following information in columns - A: midpoint of sample date and time gmt B: midpoint of sample latitude in decimal degrees C: midpoint of sample longitude in decimal degrees D:chlorophyll index E-onwards:taxa.
DOI: 10.5281/zenodo.7599863
title: Test scenario datasets

description: Test scenario datasets for SafeNcy validation. Each dataset contains input parameters and system outputs.
DOI: 10.5281/zenodo.7599862
title: Test scenario datasets

description: Test scenario datasets for SafeNcy validation. Each dataset contains input parameters and system outputs.
DOI: 10.17031/63dbc789116dd
title: Dinoflagellates counted in CPR data from 1958-2020

description: Excel file containing dinoflagellate counts from CPR data from the North Atlantic (45N to 65N -17W to 7E) between 1958 and 2020, sample level data (each sample being 1 row, equating to approx. 10 nautical miles of tow, 3m3 of filtered seawater). There are two excel sheets, the first contains the taxa abundance counts, the second contains the taxa names and if that name includes group data (1=grouped). For each taxa, values indicate abundance (according to standard CPR methodology, see Richardson et al. 2006, Prog in Oceanog 68. 27-74), zero values are absence of taxon, empty cells are when the taxon was not looked for. The excel file consists of samples in rows, with the following information in columns - A: midpoint of sample date and time gmt B: midpoint of sample latitude in decimal degrees C: midpoint of sample longitude in decimal degrees D:chlorophyll index E-onwards:taxa.
DOI: 10.6084/m9.figshare.21996884
title: ProjecTILs human reference atlas of tumor-infiltrating and blood Dendritic Cells, version 1

description: ProjecTILs human reference atlas of blood and tumor-infiltrating Dendritic Cells (DC), generated by semi-supervised STACAS integration of 11 scRNA-seq datasets spanning 5 cancer types. The datasets were obtained from Gerhard et al. JEM, 2020, as well as Villani et al. Science, 2017, for the blood samples. The dataset is available as a Seurat object, with subtype labels (functional cluster), sample of origin and several other measurement for each cell stored in the object metadata. The object can be loaded into an R session using the readRDS() function, and directly used as a reference atlas for ProjecTILs (Andreatta et al. Nat Comms 2021).
DOI: 10.6084/m9.figshare.21996884.v2
title: ProjecTILs human reference atlas of tumor-infiltrating and blood Dendritic Cells, version 1

description: ProjecTILs human reference atlas of blood and tumor-infiltrating Dendritic Cells (DC), generated by semi-supervised STACAS integration of 11 scRNA-seq datasets spanning 5 cancer types. The datasets were obtained from Gerhard et al. JEM, 2020, as well as Villani et al. Science, 2017, for the blood samples. The dataset is available as a Seurat object, with subtype labels (functional cluster), sample of origin and several other measurement for each cell stored in the object metadata. The object can be loaded into an R session using the readRDS() function, and directly used as a reference atlas for ProjecTILs (Andreatta et al. Nat Comms 2021).
DOI: 10.6084/m9.figshare.21996884.v1
title: Untitled Item

description: ProjecTILs human reference atlas of blood and tumor-infiltrating Dendritic Cells (DC), generated by semi-supervised STACAS integration of 11 scRNA-seq datasets spanning 5 cancer types. The datasets were obtained from Gerhard et al. JEM, 2020, as well as Villani et al. Science, 2017, for the blood samples. The dataset is available as a Seurat object, with subtype labels (functional cluster), sample of origin and several other measurement for each cell stored in the object metadata. The object can be loaded into an R session using the readRDS() function, and directly used as a reference atlas for ProjecTILs (Andreatta et al. Nat Comms 2021).
DOI: 10.5281/zenodo.7599831
title: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO Data sets

description: Updated 2 February 2023.<br> This archive contains the key data files (including figure data) associated with: Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, Nature Water, accepted. Figure1_time_series_data.xlsx – Data used to create the 14 inset time series plots in Figure 1. fig1_extents.zip – Contains spatial data used to create the "Top wet events" and "Top dry events" maps in Figure 1, in both text and NetCDF formats. Figure2_data.xlsx – Time series data used to create Figure 2. Figure3a_Koeppen-Geiger-ASCII.zip - Contains a text data file of spatial data (latitude, longitude, class) used to create the climate class map in Figure 1. Note that only the main climates (first letter of class code) are used: A = Tropical, B = Dry, C = Temperate, D = Continental, E = Polar (no data). See http://koeppen-geiger.vu-wien.ac.at/present.htm for details. Figure3bc_data.xlsx – Time series data used to create Figures 3b and 3c. Figure4_data.xlsx – Location, year, and intensity data used to create the maps in Figure 4. all_event_intensity.xlsx - Contains the centroid location (longitude and latitude), year, and intensity (km3mo) of all 505 wet extreme events and 551 dry extreme events identified and analyzed in this study. Source data and code used in this study are available as follows. Data Availability<br> The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas Center for Space Research (https://www2.csr.utexas.edu/grace/RL06_mascons.html). The output from a global GRACE/FO data assimilating instance of the Catchment land surface model (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11-month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GRACEDADM_CLSM025GL_7D_3.0/). The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/climateindices/list/ and https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/). The global mean temperature data are available from the NASA Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Köppen-Geiger climate map data are available for download from http://koeppen-geiger.vu-wien.ac.at/present.htm. Code Availability<br> The python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository, https://github.com/gitAtila/ST-DBSCAN. Statistical analyses were performed and figures were generated using NCL software.
DOI: 10.5281/zenodo.7585389
title: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO Data sets

description: Updated 2 February 2023.<br> This archive contains the key data files (including figure data) associated with: Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, Nature Water, accepted. Figure1_time_series_data.xlsx – Data used to create the 14 inset time series plots in Figure 1. fig1_extents.zip – Contains spatial data used to create the "Top wet events" and "Top dry events" maps in Figure 1, in both text and NetCDF formats. Figure2_data.xlsx – Time series data used to create Figure 2. Figure3a_Koeppen-Geiger-ASCII.zip - Contains a text data file of spatial data (latitude, longitude, class) used to create the climate class map in Figure 1. Note that only the main climates (first letter of class code) are used: A = Tropical, B = Dry, C = Temperate, D = Continental, E = Polar (no data). See http://koeppen-geiger.vu-wien.ac.at/present.htm for details. Figure3bc_data.xlsx – Time series data used to create Figures 3b and 3c. Figure4_data.xlsx – Location, year, and intensity data used to create the maps in Figure 4. all_event_intensity.xlsx - Contains the centroid location (longitude and latitude), year, and intensity (km3mo) of all 505 wet extreme events and 551 dry extreme events identified and analyzed in this study. Source data and code used in this study are available as follows. Data Availability<br> The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas Center for Space Research (https://www2.csr.utexas.edu/grace/RL06_mascons.html). The output from a global GRACE/FO data assimilating instance of the Catchment land surface model (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11-month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GRACEDADM_CLSM025GL_7D_3.0/). The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/climateindices/list/ and https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/). The global mean temperature data are available from the NASA Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Köppen-Geiger climate map data are available for download from http://koeppen-geiger.vu-wien.ac.at/present.htm. Code Availability<br> The python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository, https://github.com/gitAtila/ST-DBSCAN. Statistical analyses were performed and figures were generated using NCL software.
DOI: 10.5281/zenodo.7499496
title: Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data

description: Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the GitHub repository. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021. The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing. In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "&lt;URL&gt;" and "&lt;URL_DOMAIN&gt;" token respectively. User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) . Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) . In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns: pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime) effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available. rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.