Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Supporting information for: An assessment of monazite fission-track thermochronology as a proxy for low-magnitude cooling, Catalina-Rincon Metamorphic Core Complex, AZ, U.S.A.

The following supporting information contains: The detailed location and age data for the geochronological, isotopic, and geochemical data used in this study, and their associated publications. Detailed thermochronometric data and associated thermal history modelling information for all thermochronology and modelling presented in the study.

opencc-by-4.0Mar 2022View details →

Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information

Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper. Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.

opencc-by-4.0Sep 2024View details →

Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"

Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading". Name/institution/contact information: Dr. Michal Barto&scaron;ák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz. Date of data collection: The data were collected between 2021 and 2024. File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt." See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.

opencc-by-4.0May 2023View details →

Raw Counts: A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information

Raw counts for scientific article:  "A protocol for low-input RNA-sequencing of patients with febrile neutropenia captures relevant immunological information" Victoria Probst*1, Lotte Møller Smedegaard*2, Arman Simonyan1, Yuliu Guo1, Olga Østrup1, Kia Hee Schultz Dungu2, Nadja Hawwa Vissing2, Ulrikka Nygaard2 and Frederik Otzen Bagger1 *Shared first authorship Data description:  The raw counts are from 88 samples of 22 patients with leukaemia and suspected infection sequenced by a low-input protocol (Takara SMART-seq HT) (96% succeeded) and 15 of these were also processed by the standard protocol (Truseq).   CLI.CSV: Raw counts of control samples processed using a low input RNA sequencing protocol. 15 samples processed by the low-input protocol.   CRNA.CSV: Raw counts of control samples processed using a standard RNA sequencing protocol. 15 samples processed by the standard protocol.  FEB.CSV: Raw gene counts from patients. 88 samples processed by the low input protocol. 4 samples failed sequencing.

Fig. 1 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change

Fig. 1. Camera-trap image of mainland serow (Capricornis sumatraensis) from the lowlands of the Pasoh Forest Reserve in Peninsular Malaysia at an elevation of ~100 m.

Fig. 4 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change

Fig. 4. Regional-scale relationships between serow captures and covariates. Displayed are the variables within the top-performing multivariate model as assessed by lower AICc scores. All covariates are averaged at a 20-km radius around the study area.

Fig. 5 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change

Fig. 5. The relationships between serow predicted abundance and habitat variables at the local scale from Royle-Nichols hierarchical models. Oil palm, roughness and Human Footprint Index were in the top performing multivariate model.

Fig. 3 in The highs and lows of serow (Capricornis sumatraensis): multi-scale habitat associations inform large mammal conservation strategies in the face of synergistic threats of deforestation, hunting, and climate change

Fig. 3. Presence of the serow within its Southeast Asian range. Panel a) shows the IUCN Red List range extent of occurrence (EOO; shaded orange), and the occurrence records coloured by the data source. Panel b) shows the jackknife-based assessment of variable importance. The blue bars showing the explanatory power in the model using only the denoted variable, while the teal bars show the predictive power of the full model without the denoted variable, highlighting whether the variable captures unique information. Panel c) shows the probability of presence of the serow from Maxent modelling mapped within the Southeast Asian region covered by this study. Panel d) shows the forest cover in 2015 that is potentially occupied within the EOO. Panel e) is the Maxent probability of presence of serow inside the remaining forested areas within Southeast Asia. Original artwork courtesy of Tamzin Barber (https://www.talkinganimals.com.au/).

opencc-by-4.0May 2022View details →

Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"

The dataset contains the following compressed folder: -Notebooks.zip: This folder contains:   -python_script:         -RESREL.py: script performing the clusterization and computing the relevance resolution curves         -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s         -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.   -python_notebook:         -Exploratory_analysis.ipynb:  Analysis performed on the 12-protein_dataset         -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody         -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake Packages required for the usage of these python scripts/notebooks:   -numpy   -pandas   -matplotlib   -seaborn   -multiprocessing   -scipy   ======== RAW DATA ======== The raw data produced and employed in this study are available on a Google Drive folder at the following address: https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9 In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder Data.zip, within which the following data are present: -12-protein_dataset:     -md.mdp: the .mdp file used in the MD simulations     -PROTEIN_PDB_CODE:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves, sel=[all, CA, CB]         -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]         -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)         -MSR_df.csv: a dataset containing the following columns                   'area' : area behind the Relevance-Resolution curve;                   'selection': the atomic selection (['all', 'CA', 'CB']) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)                   'method': the linkage measure used in the clustering procedure, an integer in [0,6];                   'method_name': the linkage measure used in the clustering procedure, a string in ['average','ward','complete','single','centroid','median','weighted'];                   'rmsd_mean': the mean value of the rmsd vector along the trajectory computed wrt the first frame;                   'rmsd_var': the variance of the rmsd vector along the trajectory computed wrt the first frame;                   'rgy_mean': the mean value of the radius of gyration  along the trajectory;                   'rgy_var': the variance of the radius of gyration  along the trajectory;                   'rmsf_mean': the mean value of the rmsf;                   'rmsf_var': the variance of the rmsf;                   'RMSD_M_mean': the mean value of the RMSD matrix.                   'RMSD_M_var': the variance of the RMSD matrix.   -Random:     -curves.npy= 100K Relevance-Resolution Random curves for M=40001     -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000   -validation_dataset:     -antibody:         -Hk_CB.npy & Hs_CB.npy: the Rel & Res curves         -RMSD_CB.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters     -1ake:         -Hk_{sel}.npy & Hs_{sel}.npy: the Rel & Res curves         -RMSD_{sel}.npy: the RMSD matrix         -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space         -Label_{method}.npy: the label vector for n_clusters         -intra_{m}.npy: the intra-cluster covariance matrix         -inter_cov_{m}.npy: the inter-cluster correlation matrix   NOTE ===== The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py. These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.

opencc-by-4.0Oct 2024View details →

Supporting information for: The Time Requirements for Primary Care Consultations: Initial Sick Child Visits in Low- and Middle-income Countries Using the Integrated Management of Childhood Illness (IMCI) Clinical Algorithm

Few studies have examined the time required for primary care consultations; none have focused on sick child visits in low- and middle-income countries (LMICs). This project begins to fill that gap by providing evidence-based estimates of the time needed for initial visits with under-five infants and children at public or not-for-profit facilities in countries using the Integrated Management of Childhood Illness (IMCI) clinical algorithm. Estimates of the mean expected duration of IMCI consultations require (a) classification profiles, i.e., tabulations of the gold standard health issues presented by patients less than 5 years old; (b) lists of the tasks included in applicable versions of the IMCI algorithm and the conditions that elicit them, and (c) an estimate of the time needed to perform tasks with no pre-defined minimum duration. The latter requires, in addition to classification profiles, information on rates of task performance and the mean observed duration of consultations. The IMCI clinical algorithm and the research surrounding it provide unusually rich sources of such information. Developed in the mid 1990s by the World Health Organization and the United Nations Children’s Fund, the IMCI algorithm seeks to reduce child mortality in LMICs by improving the technical quality of primary care services. For infants less than 2 months old, the algorithm focuses on bacterial infections, feeding problems, low weight, and, in some versions, jaundice. For children 2-59 months old, the foci include acute respiratory infections, especially pneumonia; diarrhea; fevers, especially malaria and measles; malnutrition, and anemia. Immunization status is a concern for both age groups. The algorithm provides a scheme to classify the health issues with which infants and children present, an array of tasks providers may be expected perform, and criteria by which tasks are elicited. Research on the design and utility of the algorithm, its effects on provider performance, and related topics furnishes data on the prevalence of gold standard IMCI classifications in a variety of patient populations. In some cases, it also enables one to calculate the time required to perform tasks. I found such information by searching MEDLINE, the database of the International Network for Rational Use of Medicines, the websites of the WHO and its regional offices, GOOGLE, and GOOGLE SCHOLAR using search terms such as ‘Integrated Management of Childhood Illness’, ‘observational’, ‘prospective’, ‘classification’, ‘clinical signs’, ‘health facility survey’, and ‘validity’. I also reviewed studies that cited a qualified study and, conversely, material included in the bibliographies of qualified studies. The supplemental information files contain the following: WORKBOOK S1_STUDIES USED Lists features of, and sources for, the studies used to construct classification profiles and to estimate the time required to perform the average task with no predefined minimum duration. With 2 exceptions (see below, DATA S1 and DATA S2), all the studies have been published or are readily available on the internet. None of the data can be used to identify individuals. DATA S1_REPORT OF THE HEALTH FACILITY SURVEY IN BOTSWANA, 2007-08 and DATA S2_REPORT OF THE HEALTH FACILITY SURVEY IN TANZANIA, 2003 PDF files of Health Facility Survey reports that were found on the internet but have since been taken down. DATA S3_BURKINA FASO CHART BOOKLET, 2015 PDF provided Drs. Sophie Sarrassat (London School of Hygiene and Tropical Medicine) and Serge M. A. Somda (Université Nazi BONI). WORKBOOK S2_CLASSIFICATION PROFILES: INFANTS; WORKBOOK S3_CLASSIFICATION PROFILES: CHILDREN IN UPPER MIDDLE-INCOME COUNTRIES; WORKBOOK S4_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (I); WORKBOOK S5_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (II), and WORKBOOK S6_CLASSIFICATION PROFILES: CHILDREN IN LOW INCOME COUNTRIES                                                                                                           The design of the worksheets in these workbooks is described in TEXT S1_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES (see below). WORKBOOK S7_IMCI CLINICAL TASKS Lists the clinical tasks provided by relevant IMCI algorithms for the care of infants and children. Consists of 6 worksheets covering mandatory tasks, conditional assessments, and treatment and counseling tasks for infants and children. WORKBOOK S8_MINUTES PER TASK WITH NO MINIMUM DURATION Provides estimate of the mean time required to perform a task with no minimum duration for each of 7 populations for which the required data are available, corrected, where necessary, for the effect of an observer on the rate and pace of task performance. Also provides a geometric mean for all 7 populations. TEXT S1_NOTES ON METHODOLOGY WORD document describing the steps involved in estimating the expected durations of consultations. TEXT S2_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES WORD document describing the steps involved in constructing each profile, problems encountered, and how they were solved. TEXT S3_NOTES ON THE IDENTIFICATION OF IMCI CLINICAL TASKS WORD document describing the standards used in identifying clinical tasks in IMCI algorithms. TEXT S4_NOTES ON THE ESTIMATION OF MINUTES PER TASK WITH NO MINIMUM DURATION WORD document describing the steps involved in estimating the mean time required to perform a task with no predefined minimum duration, problems encountered, and how they were solved.

opencc-by-4.0Mar 2023View details →

Supporting information for "Kinetics of CN(v=1) reactions with butadiene isomers at low temperature by cw-Cavity Ringdown in a pulsed Laval flow with theoretical modelling of rates and entrance channel branching

This file contains the master equation inputs for all the reactions studied, as well as all the details on stationary points and VRC-TST fluxes necessary to reproduce the simulations.

Rotation Period Predictions for Low-mass Stars with Kinematic Information from Gaia DR3

<pre>DR3_kine_prot_pred.csv: 17.6 million predicted periods for all stars with Gaia DR3 RVs. This data should only be used to vet periods and not directly as period measurements. Works best for stars with GBP-GRP > 1.5 and rotation period > 20 days. Do not work for fast-rotating low-mass stars. Column descriptions see below </pre> source_id: Gaia DR3 source_id ra: Gaia DR3 ra measurements dec: Gaia DR3 dec measurements parallax: Gaia DR3 parallax measurements bp_rp: Gaia DR3 GBP-GRP measurements phot_g_mean_mag: Gaia DR3 G mag abs_G: absolute Gaia G magnitude derived from Gaia DR3 G mag and parallax ruwe: Gaia DR3 ruwe measurements  Prot_pred: Predicted periods using Gaia DR3 parameters and kinematics   vet_ztf_lowmass.csv: 65k vetted ZTF period measurements for stars with GBP-GRP > 1.5. Column descriptions see below source_id: Gaia DR3 source_id ra: Gaia DR3 ra measurements dec: Gaia DR3 dec measurements parallax: Gaia DR3 parallax measurements bp_rp: Gaia DR3 GBP-GRP measurements gmag: Gaia DR3 G mag abs_G: absolute Gaia G magnitude derived from Gaia DR3 G mag and parallax rv: Gaia DR3 rv measurements Prot: measured and vetted periods from ZTF