cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/cymv6d7cgv
title: BVD-TV Dairy Farm dataset 2024

description: Base de Datos DairyComp 2021 is a database of Argentinian DairycComp 305 users from more than 200 dairy farms that use the software DairyComp305. This data set summarize the reproductive and productive parameters for these users. Data is collecte everyday from the dairy farms to the software and then compilated into this report. Data from all the animals of the farms is collected daily in this software. Information is in spanish, but it is pretty clear to undertand.

Data BVD TV is the data collected from 6 dairy farms under study after the incorporation of Vedevax Block, a targeted subunit vaccine against BVDV (BVD-TV). Data was collected in the DairyComp 305 software, extracted and classified in Years pre-BVD TV, year 0, the year the vaccine was incroporated , and years post-BVD TV. All parameters are annualized, with the exact numbers of animals and the proportion for each parameters. Milk production was also meassure. This data reflects the effect of the Vedevax Block vaccine on the dairy farms, but it also may work as an indication of the impact of the BVD virus on the farms
DOI: 10.17632/ym6pk4889r
title: PART of GearEccDataset

description: A small PART of the GearEccDataset.
For the FULL version and more information, please refer to https://github.com/LeeJMJM/GearEccDataset.
DOI: 10.6084/m9.figshare.24802944.v1
title: Matlab code for data analysis related to CCA decoding

description: Included are one Matlab script (simpleExample.m) and two functions (bruteDecode.m and opti1DDecode.m). These codes were used to perform the data analysis described in Barreiro et al "<b>Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces</b>" journal TBD
DOI: 10.6084/m9.figshare.24540127.v2
title: Forest responses to megadisturbance

description: Data and code associated with Bendall et al. manuscript relating forest responses to megadisturbance.NOTE: Please download all files to a single folder and then open and follow the instructions in the 'README'.
DOI: 10.6084/m9.figshare.28778525.v1
title: Table S1

description: <b>Table S1. Non-significant correlations between delta V̇O2</b><sub><strong>MAX</strong></sub><b> and peripheral vascular variables of the study.</b>
DOI: 10.6084/m9.figshare.4123158.v2
title: Genome - Capsaspora owczarzaki (v3)

description: Capsaspora owczarzaki's genome assembly and annotation. VERSION 3–<strong>Arnau Sebé-Pedrós</strong>, Manuel Irimia, <strong>Javier del Campo</strong>, <strong>Helena Parra-Acero</strong><strong>,</strong> Carsten Russ, Chad Nusbaum, Benjamin J. Blencowe & <strong>Iñaki Ruiz-Trillo</strong>. (2013) <em>Regulated aggregative multicellularity in a close unicellular relative of Metazoa</em>. <em><strong>eLife</strong> 2013;2:e01287</em>www.multicellgenome.com<br>
DOI: 10.6084/m9.figshare.9633128.v1
title: EC9_B49.nwb

description: The enclosed data is collected using a high-density 256-channel electrocorticography array implanted in a human patient during treatment for epilepsy. The subjects are reading aloud consonant-vowel syllables from a list. The data was collected by Dr. Edward Chang at the University of California, San Francisco, and curated by Ben Dichter.Data is organized by subject ID, and each file is a continuous recording session in Neurodata Without Borders: Neurophysiology (NWB:N) 2.0 format. Voltage traces are included for each of the recorded 256 channels. Microphone signal was recorded at the time but is removed for HIPAA compliance. Detailed hand-marked annotations are provided which mark what syllable was said, and the times of the start, consonant-vowel transition, and end of each syllable. A rest-period time is also included when the subject was silent, which can be used as a baseline. Hand-marked anatomical labels are included for electrodes in the relevant brain regions. All dates have been removed for HIPAA compliance and replaced with Jan 1, 1900.
DOI: 10.17632/2syp3fy6zj
title: Nanomedicine as an opportunity for equity achievements through cost-reduction in public healthcare for head and neck cancer treatment in Brazil.

description: These raw data were used for the analysis of the results demonstrated in the main manuscript
DOI: 10.17632/g58jrmh4jj
title: Got Milk? The Effect of Export Price Shocks on Exchange Rates

description: Replication data for Got Milk? The Effect of Export Price Shocks on Exchange Rates by Hillary Stein
DOI: 10.6084/m9.figshare.14632776.v1
title: OpenFOAM simulation for July 9th, 2020 (Coaticook-Massawippi)

description: Everything needed to restart the July 9th simulation in OpenFOAM version 2012 (0.5 kg/m<sup>3</sup>). One only need type ./Allrun on a computer with a 64 cores. Please adjust decomposeParDict to match available cores.<br>
DOI: 10.6084/m9.figshare.22124219.v12
title: The Urbanity Global Network Dataset

description: The Global Urban Network (GUN) dataset provides pre-computed node and edge attribute features for various cities. Each layer is available in .geojson format and can easily be converted into NetworkX, igraph, PyG, and DGL graph formats.
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For node attributes, we adopt a uniform Euclidean approach, as it provides a consistent, straightforward, and extensible basis for integrating heterogeneous data sources across different network locations. Accordingly, we construct 100 metres euclidean buffers for each network node and compute the spatial intersection with spatial targets (e.g., street view imagery points, points of interest, and building footprints). To ensure spatial consistency and accurate distance computation, we project spatial entities into local coordinate reference systems (CRS). Users can employ the Urbanity package to generate Euclidean buffers of arbitrary distance. 
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For edge attributes, we adopt a two-step approach: 1) compute the distance between each spatial point of interest and its proximate edges in the network, and 2) assign entities to the corresponding edge with lowest distance. To account for remote edges (e.g., peripheral routes that are not located close to any amenities), we specify a distance threshold of 50 metres. For buildings, we compute the distance between building centroids and their respective network edge.  Accordingly, we compute spatial indicators based on the set of elements assigned to each network edge.
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We also release aggregated subzone statistics for each city. Similarly, users can employ the Urbanity package to generate aggregate statistics for any arbitrary geographic boundary. 
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Urbanity Python package: https://github.com/winstonyym/urbanity.
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DOI: 10.6084/m9.figshare.24355680.v1
title: Data Files from Endotherms trade body temperature regulation for the stress response

description: Contains all data used in this study.
DOI: 10.6084/m9.figshare.25400428.v1
title: Quixadomyces sanctacrucensis, phylogenetic dataset

description: Alignment.
DOI: 10.6084/m9.figshare.973207.v3
title: IPython 2013-2015 Sloan Grant Project Reports

description: Progress reports for the 2013-2015 grant from the Alfred P. Sloan Foundation supporting the IPython Notebook development.
DOI: 10.6084/m9.figshare.19411718.v1
title: Supplemental Material

description: Supplemental Material for 2022 mBio CRKP Publication
DOI: 10.6084/m9.figshare.26932036.v1
title: Nav1.8 Peptide Behavior (Raw Data)

description: Raw data collected from behavioral experiments performed on rats treated with Nav1.8 peptidomimetics.
DOI: 10.6084/m9.figshare.28587175.v1
title: Additional file 5 of Decreased complement 4 and interleukin-10 as biomarkers in aqueous humour for non-exudative age-related macular degeneration: a case control study

description: Additional file 5. Original data of average biomarker levels underlying heatmap presentation. Displayed are average biomarker levels in all subjects, that were adapted to the kit detection limits, in descending order of adapted arbitrary unit (AAU). For the calculation of the AAU, the mean arbitrary unit (AU) for parametric datasets or the median AU for non-parametric datasets was multiplied by the minimum detection level (pg/mL) of each protein, that was given by the certificate of analysis of each kit. Underlined proteins were parametric. Proteins were separated according to the magnitude of AAU levels. An AAU ≥ 10*105 was labelled as a high level, 10*105 &gt; AAU ≥ 1*103 as a medium level and AAU &lt; 1*103 as a low level. Some proteins included in the low level group showed probands with MFI values of zero below the detection limit. Since most probands exhibited values that exceeded the detection limit, these specific proteins were not excluded, but included in the low biomarker level group.
DOI: 10.17632/g33bhs9f2y
title: Dung beetle assemblages within Amazonian selectively logged forests

description: This dataset results from the MSc research in Applied Ecology, entitled "Selective logging in Amazonian forests: post-logging recovery in dung beetle communities", by Filipe França at the Federal University of Lavras. It includes data on [1] dung beetle species richness, abundances and biomass; [2] dung beetle-mediated ecological processes of dung removal, seed dispersal and soil bioturbation; and [3] environmental conditions related to canopy openness, soil texture (sand, silt and clay content), and disturbance intensity (number of trees and volume of timber removed). Datasets were surveyed using a space-for-time experimental design, between January and March 2010, across 49 10-ha logging management units distributed along a gradient of selective logging intensities within two areas (Bituba and Gueti) in the Jari region of the Brazilian Amazon, state of Pará. Surveyed areas had different times since logging: Bituba was managed in 2005 and Gueti in 2009, with logging intensities varying between 0-76 and 0-55 trees removed, and 0-643 and 0-669 timber volume (m3/10ha), respectively.
We followed the harvest operational plans of the timber concession, which adopts 250×400m logging management units, to establish six sampling points spaced 100m apart in a 2×3 rectangular grid and around 75 m from the unit edges. We sampled dung beetles using baited pitfall traps (40g of dung; 19 cm width, 11 cm depth; 1 trap/sampling point, 6 traps/unit). We surveyed ecological processes using two 1-m diameter mesocosm arenas placed at the middle sampling points of the rectangular grid 24h before installing pitfall traps. Each arena was a circular plot delimited by a 15-cm high nylon net fence (area of ~0.79m2) and received a single 200-g experimental dung deposit mixed with seed mimics of three sizes: 10 large (15.5-mm diameter), 20 medium (8.6-mm) and 50 small (3.5-mm). All dung used had a 4:1 pig to human ratio to increase attractiveness and allow enough baits to carry out all field experiments. After 24h of exposure, we weighted the remaining dung and the loose soil found above the soil surface (if present) to assess rates of dung removal and soil bioturbation. Rates of seed dispersal are based on the difference between total seed mimics placed in each dung deposit and the number of seed mimics found in the remaining dung (when present).  At each sampling point, we took a (1) soil sample (up to 10cm depth) and (2) hemispherical photograph using a Nikon FC-E8 fisheye lens attached to a Nikon D40 camera levelled ∼1.20m from the ground. We formed a composite soil sample to represent the soil granulometry (clay, silt, fine sand, and coarse sand; g/Kg) within each sample unit, which was analysed in the soil laboratory of Jari Celulose S.A. We used the Gap Analyser software to estimate the canopy openness (%), which represents the total open area of the hemispherical photographs. For further details, please see related links and 'README'  in the attached spreadsheet.
DOI: 10.17632/pjv2yr7pvr
title: SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials

description: The molecular dynamics (MD) simulation is a favored method in materials science for understanding and predicting material properties from atomistic motions. In classical MD simulations, the interaction between atoms is described by an empirical interatomic potential, so the reliability of the simulation hinges on the accuracy of the underlying potential. Recently, machine learning (ML) based interatomic potentials are gaining attention as they can reproduce potential energy surfaces (PES) of ab initio calculations, with a much lower computational cost. Therefore, an efficient code for training ML potentials and inferencing PES in new configurations would widen the application range of MD simulations. Here, we announce an open-source package, SNU Interatomic Machine-learning PotentiaL packagE-version Neural Network (SIMPLE-NN) that generates and utilizes the ML potential based on the artificial neural network with the Behler–Parrinello type symmetry function as descriptors for the chemical environments. SIMPLE-NN uses the Atomic Simulation Environment (ASE) package and Google Tensorflow for high expandability and efficient training, and also supports the in-house code for quasi-Newton method. Notably, the package features a weighting scheme based on the Gaussian density function (GDF), which significantly improves accuracy and reliability of ML potentials by resolving sampling bias that exists in typical training sets. For MD simulations, SIMPLE-NN interfaces with the LAMMPS package. We demonstrate the performance and usage of SIMPLE-NN with examples of SiO2.
DOI: 10.17632/xnrgv2z76h
title: MLAnalysis: An open-source program for high energy physics analyses

description: We present a python-based program for phenomenological investigations in particle physics using machine learning algorithms, called MLAnalysis. The program is able to convert LHE and LHCO files generated by MadGraph5_aMC@NLO into data sets for machine learning algorithms, which can analyze the information of the events. At present, it contains three machine learning (ML) algorithms: isolation forest (IF) algorithm, nested isolation forest (NIF) algorithm, kmeans anomaly detection (KMAD), and some basic functionality to analyze the kinematic features of a data set. Users can use this program to improve the efficiency of searching for new physics signals.
DOI: 10.6084/m9.figshare.20509470.v1
title: Raw data for Leptin deficient ob/ob mice exhibit periodontitis phenotype, and altered oral microbiome

description: The raw data for manuscript Leptin deficient ob/ob mice exhibit periodontitis phenotype, and altered oral microbiome
DOI: 10.6084/m9.figshare.24541912.v1
title: Source Data and Code

description: This is the figure dataset and data analysis code of the submitted paper entitled: "Reactive Aldehyde Chemistry Explains the Missing Source of Hydroxyl Radicals".
DOI: 10.6084/m9.figshare.27135216.v4
title: Evaluation of a six-minute walk test in the DE50-MD canine model of Duchenne muscular dystrophy and its effect on blood-borne biomarkers - version 1

description: "<b>Evaluation of a six-minute walk test in the DE50-MD canine model of Duchenne muscular dystrophy and its effect on blood-borne biomarkers</b>"Underlying data:6MWT - raw data fullBlood biomarker - all raw dataBlood biomarker - key biomarker summaryBlood biomarker - MYOM3 raw dataBlood biomarker - MSTN raw dataBlood biomarker - microRNA RT-qPCR raw data<br>Extended data:6MWT - supplementary dataBlood biomarker - supplementary biomarker summaryE10 checklist - 6MWT Riddell et al
DOI: 10.17632/kt25vc4mrv
title: Data for: The effect of the Internet on economic Growth: Counter-evidence from cross-country panel data.

description: Data used in Maurseth, P. B. "The effects of the Internet on economic Growth: Counter-evidence from cross-country panel data"
DOI: 10.6084/m9.figshare.28136420.v1
title: Validation of the Thai version of the World Health Organization’s Quality of Life Scale (WHOQOL-BREF-THAI) among Thai Nursing Students in Northeast Thailand: A multi-canter study

description: Background: The Thai version of World Health Organization Quality of Life Scale (WHOQOL-BREF-THAI) has been verified among a variety of populations. However, existing research lacks comprehensive psychometric properties, including factor structure, specifically among nursing students. This study examined the WHOQOL-BREF's unique psychometric features for the Thai nursing students in Northeast Thailand.Methods: A cross-sectional study recruited the Thai nursing students using multi-stage simple random sampling from 15 universities located in the Northeast of Thailand. The data was collected via self-assessment questionnaires, and the 3,570 participants were then randomly split into two subsamples (group 1, n=2,000, and group 2, n=1,570). After reducing some items using statistical methods, an exploratory factor analysis (EFA) was carried out on group 1 to explore the factor structure of the WHOQOL-BREF-THAI. Finally, group 2 was used in a confirmatory factor analysis (CFA) to validate the EFA's modified structure along with an assessment of the construct validity of the WHOQOL-BREF-THAI.Results: A total of 3,570 Thai nursing students were enrolled. Principal component analysis in a random subsample supported a four-factors model with 24 items was originally suggested for the factorial construct validity, and contained 24 items, spread across four components as follows: physical (7 items), psychological (6 items), social relationship (3 items) and environmental (8 items). The purpose domain structure showed good fit in CFA in the other subsample. Internal reliability was satisfactory (Cronbach's alpha was greater than 0.70) for both the total scale and subscales. In terms of convergent validity, average variance extracted (AVE) revealed that all WHOQOL-BREF-THAI subscales achieved convergence effect with AVE ranging from 0.50 to 0.69.Conclusion: These results reveal that the WHOQOL-BREF-THAI is a valid and reliable tool for health care providers to utilize to measure QOL among Thai nursing students. QOL is also likely to have an impact on their future clinical success. Therefore, the WHOQOL-BREF can be an essential inventory for researching the factors influencing nursing students'QOL, with implications for nursing education.<br>
DOI: 10.17632/zmzjdndp2d
title: Data for: The genes polymorphism of angiotensin-converting enzyme (ACE) I/D and ACE G2350A in patients with left ventricular hypertrophy: a meta-analysis

description: ACE ID and ACE G2350A genes polymorphism in patients with left ventricular hypertrophy.
DOI: 10.6084/m9.figshare.11378745.v2
title: Comparing laboratory-based and population-based antimicrobial resistance surveillance in urinary tract infections in Indonesia

description: The project assessed the difference in point prevalence of antimicrobial resistance (AMR) when using routine laboratory-based surveillance and active population-based surveillance (survey). The data are restricted to the prevalence of AMR in isolates of<i> Escherichia coli</i> and <i>Klebsiella pneumoniae</i> cultured from urine samples from all patients presenting with signs and/or symptoms of a urinary tract infection in either the outpatient or the inpatient setting. <br><br>Laboratory-based surveillance used Vitek methodology to assess resistance, while the population-based surveillance used the conventional culture methods using MacConkey agar plates incubated for 18 hours. Resistance was defined by applying CLSI breakpoints (2012).. The data from both sources covered exactly the same period (1 April 2014 - 31 May 2015) in which there were no reports of outbreaks in the hospital setting.<br>Data structureThe file is in a .csv format outputted through STATA version 16.0. The dataset is organised in the long format with the combination of the variables ID(), micro-organism(), and antibiotic() identifying a unique record.<br>
DOI: 10.6084/m9.figshare.23607666.v1
title: patient50.rar

description: A Patient with fundus images that taken by Ret-cam.
DOI: 10.6084/m9.figshare.24899421.v1
title: Gene function annotation of <i>Helicoverpa assulta</i>

description: We used eggNOG-mapper v2.1.12 for functional annotation. Additionally, we searched the Uniprot database using Blastp v2.14.1+ and the Pfam and KOfam databases using HMMER v3.4.
DOI: 10.6084/m9.figshare.26827447.v1
title: temperature data.csv

description: Comparison between carbonated water immersion and tap water immersion for body cooling.
DOI: 10.6084/m9.figshare.28060172.v1
title: data for conditions (1)-(32)202412

description: 幼儿园宿舍二氧化碳和气溶胶的测试数据。
DOI: 10.6084/m9.figshare.28582490.v1
title: pan cancer analyses of KLF4

description: Pan-cancer analyses refer to studies that explore data across a wide range of cancer types to identify patterns, mutations, and commonalities that may be associated with particular genes or pathways. KLF4 (Kruppel-like factor 4) is a transcription factor known for its role in regulating cell proliferation, differentiation, and apoptosis, and it has been implicated in various cancers. A pan-cancer analysis of KLF4 would focus on its expression, mutation status, and its involvement in different types of cancers, such as:<b>Gene Expression Analysis</b>: Pan-cancer datasets typically include the expression levels of KLF4 across various cancer types, comparing tumor tissue to normal tissue. High or low expression of KLF4 could be associated with different prognoses or cancer subtypes.<b>Mutation Analysis</b>: Studies often investigate mutations or genetic alterations in KLF4 across multiple cancer types. This could involve point mutations, deletions, amplifications, or translocations that affect KLF4's function. Mutations in KLF4 might impact its role in cell cycle regulation, apoptosis, or cancer stem cell maintenance.<b>Survival Analysis</b>: The analysis might include correlations between KLF4 expression or mutations and patient survival outcomes (e.g., overall survival or progression-free survival). Higher or lower levels of KLF4 expression may correlate with better or worse prognoses, depending on the cancer type.<b>Pathway Enrichment</b>: KLF4 may be involved in specific cellular pathways like the Wnt/β-catenin signaling pathway, which plays a role in cancer development. Pan-cancer analysis may investigate how KLF4 interacts with other oncogenes and tumor suppressor genes, examining whether it activates or suppresses tumor growth.<b>Copy Number Variation (CNV)</b>: KLF4 may be subject to CNVs in some cancers, with amplifications or deletions leading to aberrant expression. Pan-cancer studies often look at how these variations affect KLF4 function and their potential contribution to oncogenesis.<b>Tumor Microenvironment (TME)</b>: KLF4 expression can also influence the tumor microenvironment, affecting immune cell infiltration, stromal remodeling, and metastasis. Pan-cancer analyses may explore these associations.
DOI: 10.6084/m9.figshare.6837956.v3
title: inDelphi-LibA+LibB-processed-indel-data

description: This folder contains processed data of indels from our experiments in two libraries of 2000 designed sequences for the inDelphi work, using code from https://github.com/maxwshen/indelphi-dataprocessinganalysis.<br><br>Note: mES-Cas9-Tol2-BioRep1-r1.csv is misnamed, it should be mES-Lib1-Cas9-Tol2-BioRep1-r1.csv.
DOI: 10.17632/y2s635w8wd
title: Grantha dataset

description: Grantha script is the script used in southern part of Ancient India to write Sanskrit Language.Sanskrit was considered as the royal language in India.Most of the literature work,ancient valuable documents,documents of ancient science,mathematics ,cultural works were written in Sanskrit Language.This data set consists of some the grantha characters segmented from palmleaves. There are a lot of palm leaves in archaeological museums in Kerala consisting of grantha characters.These palm leaves are in a state of decay,therefore the images of the grantha characters are almost degraded.
DOI: 10.6084/m9.figshare.20044337.v2
title: ClimaticVariables

description: Complete parameter list and metadata
DOI: 10.6084/m9.figshare.9767057.v1
title: Online experiment of contrast thresholds

description: The dataset of the online experiment of contrast thresholds.