cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sxb88y49jw
title: Ceruloplasmin variants might have different effects in different iron overload disorders.

description: Raw images and dataset of densitometric analysis used for the generation of the figure 1 of the paper by Pelucchi S, Ravasi G, Piperno A. Ceruloplasmin variants might have different effects in different iron overload disorders. J Hepatol. 2021, 75:1003-1004. doi: 10.1016/j.jhep.2021.05.005. 
This paper is associated to the project "Blood-cerebrospinal fluid-barrie defect as gate for targeting by liver-directed ceruloplasmin gene replacement therapy in the rare disease aceruloplasminemia" supported by Italian Ministry of Health, with the grant RF-2018-1236671 to Massimo ALESSIO as principal investigator at the IRCCS-San Raffaele Hospital, Milano, Italy.
DOI: 10.25452/figshare.plus.25114970.v1
title: Dataset supporting Figure S3_1 in "Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice"

description: Contains data from a single 1-photon widefield imaging experiment and a single Thorlabs mesoscope 2-photon imaging session from a dorsal mount mouse with online fast-z motion correction, corresponding to Figure S3, session _1. Included files contain imaging data, behavioral data, and python files with combined neurobehavioral data.Note that session names have the following format: "mouse#_bigEndianDate_cage#_info_session#_attempt#".Raw mesoscope imaging data is included in ScanImage rendered format as single big tiffs with the following nomenclature: "filename_2D.tiff".Mouse face and body cam images are included as standalone or concatenated .avi movie files, and behavioral data is included both as Spike2 files (smrx) and in exported form at Matlab data files (.mat).In all cases the first frame of the 2-photon movie, the right face/body movie, and Spike2 data are aligned to the first Labview-issued frameclock trigger (also recorded in Spike2, along with all other frameclock events). 2-photon triggers were sometimes incorrectly recorded in Spike2 (generally we recorded these as both events and waveforms), but were in all cases additionally exported from ScanImage tiff metadeta as timestamps (csv files ending in <i>header.csv). </i>Session start-time timestamps, also exported from ScanImage tiff metadata, appear as .txt files ending in "_starttime.txt".Preprocessed data (python) can be found in npy files with various names, each containing different subsets of variables relevant to the analysis. For each session, the npy file containing the string "standard_frames" contains the most complete, final stage set of preprocessed neurobehavioral data (in combined DataFrame format, exportable to nwb), including CCF/MMM alignments. The file containing the string "nb_dump" contains a large set of auxilliary variables that may be needed for additional preprocessing.Additional image files (tiff, png) and excel worksheets (xlsx, csv) containing high-level data summaries and records of intermediate analysis steps are also included.Please contact the authors for any additional clarifications as needed.See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.7052513
DOI: 10.15482/usda.adc/1529408
title: Growth and Yield Data for the Bushland, Texas, Cotton Datasets

description: This dataset consists of growth and yield data for each season when upland cotton [Gossympium hirsutum (L.)] was grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 2000 through 2004, 2008, 2010, 2012, and 2020  seasons, cotton was grown on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field also planted to cotton. The square fields were themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field were thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Cotton was grown on different combinations of fields in different years. When irrigated, irrigation was by linear move sprinkler system years before 2014, and by both sprinkler and subsurface drip irrigation in 2020. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigation at rates established as percentages of full irrigation ranging from 33% to 75% depending on the year.
The growth and yield data typically include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, boll mass (when present), lint mass, seed mass, final yield, and lint quality. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from only manual sampling on replicate plots in each field and lysimeters.
These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.
See the README for descriptions of each data file.
DOI: 10.17632/mjzfskw9hh
title: cars

description: The car dataset is a comprehensive collection of various attributes related to cars, encompassing technical specifications, features, and performance metrics. It is widely used in numerous analyses such as machine learning, data visualization, and statistical evaluation. Key attributes within this dataset include the car's make and model, manufacturing year, engine size in liters, the number of cylinders, and the type of fuel used (e.g., petrol, diesel, electric). Additionally, it includes performance indicators like horsepower, type of transmission (automatic or manual), drivetrain (FWD, RWD, AWD), fuel efficiency measured in miles per gallon, market price in USD, and the car's total mileage.

This dataset serves multiple purposes, from predicting car prices based on various attributes to analyzing factors that influence fuel efficiency. It is also useful for understanding market trends and consumer preferences over different years and models, as well as classifying cars into different categories such as luxury, economy, or sports. Example entries might list a Toyota Corolla from 2020 with a 1.8-liter engine and an automatic transmission, highlighting the dataset's diversity.

Sources of such data typically include government databases, automotive websites, and industry research studies. In machine learning, the dataset can be employed for regression analysis to predict continuous outcomes like prices or fuel efficiency, classification tasks to categorize cars, and clustering to group similar cars for market segmentation. The car dataset's extensive range of attributes makes it a valuable tool for both practical and theoretical applications in automotive analysis.
DOI: 10.6084/m9.figshare.12045102.v1
title: Stimulus selective stop-signal

description: Selective stopping dataset for the study of the influence of perceptual discrimination difficulty over strategy choice
DOI: 10.17632/p9cr9x24bw
title: Microbenthos of subarctic intertidal beaches with different morphodynamics

description: Diversity and composition of benthic diatom algae and ciliates were studied at several beaches along the White and Barents seas, from highly exposed, reflective beaches with coarse-grained sands to sheltered, dissipative silty-sandy flats. The data set adresses the objective to estimate the possible influence of the morphodynamic state and/or the sediment properties on the microbenthos (diatom algae and ciliates) of various high-latitude sandy beaches from the White and Barents Sea coasts.
DOI: 10.17632/t5hghpkn4p
title: Loss Domain in a Bank Run Context: An Experimental Approach

description: Data sets and R-manuscript
DOI: 10.6084/m9.figshare.27798756.v1
title: Dataset for the water retention and gas permeability tests

description: This dataset provides a comprehensive summary of systematic experimental results for water retention and gas flow tests across a diverse range of soil types, including loess, clay, sand, and loam. It serves as a valuable resource for verifying theoretical methods related to these topics.
DOI: 10.6084/m9.figshare.14247959.v1
title: AutoMated tool for Antimicrobial resistance Surveillance System plus (AMASSplus)

description: AMASSplus<br>Release date: 25<sup>th</sup> March 2021<br>The AMASSplus application is an extension of AMASS Version 1.1 that was released on 12 January 2021. AMASSplus has an additional report on selected notifiable bacterial diseases, and the organisms included are:<br><br>- Burkholderia pseudomallei<br>- Brucella spp.<br>- Corynebacterium diphtheriae<br>- Neisseria gonorrhoeae<br>- Neisseria meningitidis<br>- Salmonella enterica serotype paratyphi<br>- Salmonella enterica serotype typhi <br>- Non-typhoidal Salmonella spp.<br>- Shigella spp.<br>- Streptococcus suis<br>- Vibrio spp.<br> <br>AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open−access and easy−to−use application that allows a hospital to perform data analysis independently and generate isolate−based and sample−based surveillance reports stratified by infection origin from routinely collected electronic databases. The application was built in R, which is a free software environment. The application has been placed within a user−friendly interface that only requires the user to double−click on the application icon. <br>AMASS performs data analysis and generates reports automatically. The raw data files required are hospital admission and microbiology databases. Firstly, the application cleans the microbiology data file and produces isolate−based and sample−based surveillance reports without stratification by infection origin. Secondly, the application then merges the microbiology and hospital admission data files, analyzes the merged data, and produces an isolated−based and sample−based surveillance reports with stratification by infection origin. Finally, the application then perform a statistical analysis to estimate all−cause mortality of patients with AMR infection and mortality attributable to AMR, which are automatically added into the AMR surveillance report.<br>AMASS aims to support the local hospitals including those that have only microbiology results recorded in electronic formats. For example, only isolate−based surveillance report without stratification by infection origin will be generated, if only microbiology data file is available and hospital admission data file is not available in the computer.<br> This is the AMASSplus.zip containing the AMASSplus application. Further, details on how to use the application can be found in https://www.amass.website<br>If you have any questions about AMASS or AMASSplus, please contact:<br>For technical information:<br>Chalida Rangsiwutisak (chalida@tropmedes.ac),<br>Cherry Lim (cherry@tropmedres.ac), and<br>Direk Limmathurotsakul (direk@tropmedres.ac)<br> <br>For implementation of AMASSplus at your hospitals in Thailand:<br>Preeyarach Klaytong (preeyarach@tropmedres.ac)<br>Viriya Hantrakun (viriya@tropmedres.ac)<br>
DOI: 10.6084/m9.figshare.26089253.v1
title: HiGTS_2014s1

description: HiGTS is a high temporal resolution global gridded dataset of human thermal stress metrics. Derived using climate variables from ERA5-Land and ERA5, the HiGTS consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily Thermal Stress Duration (TSD) at 0.1° × 0.1° spatial resolution over the global land surface.HiGTS_2014s1 contains the data from January 1, 2014 to March 31, 2014.
DOI: 10.17632/7prdhgynth
title: Blockchain and Automotive Manufacturing

description: The dataset explores the impact of artificial intelligence (AI) on supply chain management (SCM) in the automotive sector, combining responses from 140 quantitative surveys and insights from two in-depth qualitative interviews. It captures demographic details, organisational readiness, AI adoption trends, and its influence on key performance indicators like delivery times, inventory turnover, and cost efficiency. Survey questions address AI applications, benefits, challenges, and ethical concerns such as privacy violations, workforce displacement, and data bias. Interviews provide nuanced perspectives on leadership roles, implementation hurdles, and future strategies like digital twins and autonomous logistics. The dataset offers a comprehensive analysis of AI's transformative role in SCM, highlighting its operational benefits, strategic challenges, and ethical implications, making it a valuable resource for researchers and industry practitioners.
DOI: 10.6084/m9.figshare.7166453.v1
title: SDU dataset

description: SDU makale<br>
DOI: 10.17632/fywd4k7n32
title: Cylinder in Crossflow

description: Simulation results for cylinder in cross-flow for training ML models. train_data.e contains results for Re={100,200,300,400} test_data_1.e contains results for Re=150 test_data_2.e contains results for Re=250 test_data_3.e contains results for Re=350
DOI: 10.17632/htmxz8h3xd
title: Standalone relativistic continuum wavefunction solver

description: Abstract A modification of the relativistic continuum wavefunction solver [W.F. Perger et al., Comput. Phys. Commun. 76 (1993) 250] is described. The additional angular structure needed for some open shell systems is now read from an extra input file to enhance the set of treatable systems. The convergence behavior is significantly improved by choosing a hydrogenic continuum wavefunction as starting point for the self-consistent field process. Results of the calculated Hg6sϵsJ=1 continuum are compared... Title of program: CONTWVSA version 1.0 Catalogue Id: AANC_v3_0 [ADPC] Nature of problem The relativistic Dirac-Fock equations are set up and solved numerically for continuum wavefunctions. Versions of this program held in the CPC repository in Mendeley Data AANC_v1_0; MCDF; 10.1016/0010-4655(80)90041-7 AANC_v2_0; CONTWVG; 10.1016/0010-4655(93)90136-Z AANC_v3_0; CONTWVSA version 1.0; 10.1016/S0010-4655(01)00208-9 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.15468/dl.6tx57u
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anchotatus peruvianus Brunner von Wattenwyl, 1890"
 ]
}

 The dataset includes 25 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013013-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6pgdf3zrp3
title: Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons

description: Electrophysiology recording data for "Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons"
DOI: 10.17632/9zx2pp6v4w
title: Data for: Data: Inventory of trees in five fragments of temperate evergreen forest located on the eastern slope of Chile's coastal mountain range

description: The data set comes from a tree inventory conducted in an evergreen forest fragment (dominated by Laureliopsis philippiana and Eucryphia cordifolia) and four fragments dominated by Nothofagus obliqua.
DOI: 10.17632/dghdbmggc4
title: Data for: Stress and the brain transcriptome: identifying commonalities and clusters in standardized data from published experiments

description: Supplementary Dataset 1. The dataset contains all retrieved data (full preprocessed dataset), information concerning experimental design and final result of re-annotation and gene name standardization.
Supplementary Table 1. Summary of experiments included in the analysis. It also contains numerical codes used for identification of individual studies and transcriptomic comparisons concerning genotype, sex and stress-sensitivity of tested subjects, applied stress procedures, and analyzed brain areas. 
Supplementary Table 2. Ranking of stress-responsive genes. 
Supplementary Table 3. Referential list of GC-responsive genes.
Supplementary Table 4. Calculation of gene expression enrichment in various cell types associated with the mouse nervous system based on data retrieved from http://mousebrain.org/genesearch.html. The analysis is restricted to genes belonging to choroid, meningeal and opalin cluster.
Supplementary Table 5. Ranking of genes depending on duration of stress.
Supplementary Table 6. Characteristics of stress-control comparisons containing genes from choroid and meningeal clusters. Included are only transcriptomic comparisons that contained at least 6 genes from either choroid or meningeal clusters.
Supplementary Table 7. Ranking of genes with altered expression in animals that are vulnerable and resistant to stress. 
Supplementary Table 8. Sex-specific genes that are responsive to stress. 
Supplementary Table 9. Ranking of genes that are found both in animal models of stress and human PTSD patients. 

Supplementary Cluster Files 1. Cluster analysis of stress-responsive genes. Use Java Treeview software (https://sourceforge.net/projects/jtreeview/files/) to visualize data following guidelines provided in Supplementary Fig 2. List of experiments and their codes is available in Supplementary Table 1. 
Supplementary Cluster Files 2. Cluster analysis restricted to amygdala. 
Supplementary Cluster Files 3. Cluster analysis restricted to hippocampus. 
Supplementary Cluster Files 4. Cluster analysis restricted to nucleus accumbens. 
Supplementary Cluster Files 5. Cluster analysis restricted to prefrontal cortex. 
Supplementary Cluster Files 6. Cluster analysis restricted to mice. 
Supplementary Cluster Files 7. Cluster analysis restricted to rats. 
Supplementary Cluster Files 8. Cluster analysis restricted to acute stress. 
Supplementary Cluster Files 9. Cluster analysis restricted to prolonged stress (longer than 1 week). 
Supplementary Cluster Files 10. Cluster analysis restricted to stress of medium duration (from 2 to 7 days). 
Supplementary Cluster Files 11. Cluster analysis restricted to stress resilient animals 
Supplementary Cluster Files 12. Cluster analysis restricted to stress vulnerable animals.
DOI: 10.6084/m9.figshare.10437962.v1
title: THE ROLE OF DIFFUSION-WEIGHTED MRI OF PATIENTS WITH SPINE METASTASES

description: ABSTRACT Objective: The role of diffusion-weighted MRI in differential diagnostics and predicting the survival of patients with spine metastases was studied. Methods: The study included data from MRI and morphological studies of 23 patients with spine metastases. Results: The values obtained for the apparent diffusion coefficient (ADC) of tumors were compared with their histological type, cell density and Ki-67 proliferation index. The effect of ADC values on overall patient survival was also assessed. A reliable inverse correlation was established between ADC values and Ki-67 proliferation index for various types of spine metastases (r=-0.753, p=0.017). The dependence of ADC values and overall survival of patients with metastases in the spine is shown. Conclusion: The technique of diffusion-weighted MRI can be used as part of a comprehensive assessment in the preoperative planning of surgical treatment, and as a prognostic factor of overall survival for this group of patients. Level of Evidence II. Prognostic retrospective study,
DOI: 10.6084/m9.figshare.21746856.v2
title: Data_RSTA-2022-02226_AlamGhosh-original.xls from Unified torque scaling in counter-rotating suspension Taylor–Couette flow

description: Torque measurements are reported for the Taylor–Couette flow of a neutrally buoyant non-colloidal suspension in the counter-rotation regime upto a particle volume fraction of ϕ =0.2. A unified scaling relation for the dimensionless torque (the pseudo-Nusselt number) of the form Nu<sub>ω</sub>0=(α<sub>0</sub>+α<sub>1</sub> Ta<sub>1</sub><sup>*α<sub>2</sub></sup>) Ta(0)<sup><i>k</i></sup>μ<sub><i>r</i></sub>(ϕ)<sup><i>n</i></sup> is shown to hold over a range of Taylor number Ta<sup><i>c</i>1</sup>≤ Ta ≤ ~10<sup>6</sup> that covers primary, secondary and tertiary bifurcating states; here, Ta<sub>1</sub><sup>*</sup>= Ta(ϕ)/Ta<sup><i>c</i>1</sup>(ϕ) is the reduced Taylor number, Ta<sup><i>c</i>1</sup> is the critical Taylor number at primary bifurcation and μ<sub><i>r</i></sub>(ϕ) = μ(ϕ)/μ(0) is the relative viscosity of the suspension. Possible effects of flow transitions and inhomogeneous distribution of particles on torque scaling are discussed.This article is part of the theme issue ‘Taylor–Couette and related flows on the centennial of Taylor’s seminal <i>Philosophical Transactions</i> paper (Part 1)’.
DOI: 10.6084/m9.figshare.27927438.v1
title: Unmanned Ground Vehicle for the Identification of Trees Infested by Bark Beetles

description: This research presents an Unmanned Ground Vehicle for the identification of infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoiding. Identification of infested trees is carried out by classifying the resin stains on the bark of unhealthy trees with a computer vision algorithm. This approach proposes tracking bark beetle spread in forest trees with image data of the infested trees considering resin sprouts as early indicators of the infestation in contrast to aerial monitoring which only detects trees in advanced stages. Terrain autonomous vehicle direction is controlled by changing the velocities left- and right-side wheel. A rotating LiDAR sensor is used to detect trees and avoidance objects. The dynamic model of the vehicle is presented and a control algorithm is proposed to control the path following and the stability proof is provided by a Lyapunov function. In order to show the performance of the algorithms of control and classification experimental results are presented in outdoor forest environment.