cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/w35fvmtnf2
title: Centrality and seed-based correlation maps obtained with functional MRI

description: The data set offers individual brain connectivity maps of 48 patients with functional movement disorder (FMD) and 65 healthy controls obtained with functional MRI (Mueller, et al., 2022). For each participant, the data set provides five variants of network centrality maps and two types of seed-based correlation maps using the precuneus and the left temporoparietal junction (TPJ) as seed regions.

The data set contains seven zip-files related to seven types of brain connectivity maps (five variants of brain network centrality and two types of seed-based connectivity maps):
‘GCOR.zip’: global correlation;
‘GCOR_no_denoising.zip’: global correlation without using denoising in image pre-processing;
‘ICC.zip’: intrinsic connectivity;
‘ECM_add.zip’: eigenvector centrality with the ‘add’ approach adding one to all correlations;
‘ECM_rlc.zip’: eigenvector centrality with the ‘rlc’ approach using the rectified linear unit filter;
‘SBC_precuneus.zip’: seed-based correlation with precuneus as seed-region;
‘SBC_TPJ.zip’: seed-based correlation with the left TPJ as seed-region;

After unzipping the files, there will be seven directories with the same names: ‘GCOR’, ‘GCOR_no_denoising’, ‘ICC’, ‘ECM_add’, ‘ECM_rlc’, ‘SBC_precuneus’, and ‘SBC_TPJ’.

Each of the seven directories contains a subdirectory ‘*_data’ containing all the individual centrality/correlation maps in the Neuroimaging Informatics Technology Initiative (NIfTI) file format. Patient maps are named with ‘FPH??_*.nii’ while control subjects are denoted by ‘CSS??_*.nii’ and ’CSZ??_*.nii’. The notation ‘??’ stands for consecutive numbering.

Each of the seven directories contains a subdirectory ‘SPM_fullmodel’ including an SPM12 analysis with group comparisons, e. g. the comparison of connectivity between FMD patients with and without FW, or between FW patients and healthy controls. In addition, each of the five centrality directories contains a further subdirectory ‘SPM_corr_SFMDRS’ including an SPM12 analysis for investigating a potential relationship between centrality and disease severity using the simplified FMD rating scale (SFMDRS).

All analysis subdirectories contain files of a standard SPM12 analysis including the ‘SPM.mat’. Parameter estimates are shown in the files ‘beta_00??.nii’, T-maps are named with ‘spmT_00??.nii’, and contrast images are named with ‘con_00??.nii’ (‘??’ stands for consecutive numbering). The analysis space (aka ‘brain mask’) is saved in the file ‘mask.nii’. Tables listing all clusters, coordinates, p-, T-, and Z-values for all available contrasts are also saved in the postscript file ‘spm_2022Feb25.ps.

Reference:
K. Mueller, F. Růžička, M. Slovák, Z. Forejtová, P. Dušek, P. Dušek, R. Jech, T. Serranová, Symptom-severity-related brain connectivity alterations in functional movement disorders, Neuroimage: Clinical (2022), In Press.
DOI: 10.6084/m9.figshare.14999976.v2
title: Data containing seed germination, seedling emergence, seedling establishment, and seedling recruitment of eight Ficus species (four hemiepiphytic and four non-hemiepiphytic species) from Subtropics of Southern China

description: Data from Castillo-Diaz et al. 2021 contains seed germination, seedling emergence, seedling establishment and seedling recruitment of eight Ficus species (four hemiepiphytic species and four non-hemiepiphytic species). Fresh mature syconia (i.e., fruits) were randomly collected from a minimum of five trees for each species in Yunnan and Guangxi provinces during 2018 and 2019. Seeds were submitted experimentally to environmental field conditions such as canopy light availability (gap versus non-gap), soil temperature, soil moisture and soil pH along an elevation gradient spanning from 400 m. a.s.l to 1850 m. a.s.l. in four subtropical forests in southern China.
DOI: 10.6084/m9.figshare.19681410.v1
title: A global dataset of mesophyll conductance measurements and accompanying leaf traits

description: This dataset contains a compilation of published measurements of leaf mesophyll conductance (<em>g</em><sub>m</sub>) and accompanying leaf structural, anatomical, biochemical, and physiological traits as presented in: Knauer, J., Cuntz, M., Evans, J.R., Niinemets, Ü., Tosens, T., Veromann-Jürgenson, L.-L., Werner, C. and Zaehle, S. (2022), Contrasting anatomical and biochemical controls on mesophyll conductance across plant functional types, <em>New Phytologist</em>, doi:10.1111/nph.18363. Note that the compilation aims to represent unstressed, young, but fully expanded and high light-adapted leaves, albeit these criteria were not always explicitly stated. The reported measurements are assumed to be independent (i.e. from different set of plants) if one or several of species, cultivar/variety/genotype, population, measurement year (if annual/deciduous), age class (if woody), or growth environment is different. Only one (aggregated) <em>g</em><sub>m</sub> value per set of plants is reported in the dataset. For further details on data collection and processing see reference above. <br> The file <strong>gm_dataset_Knauer_et_al_2022.xlsx</strong> contains the following sheets: <strong>- data</strong>: the main dataset including all variables and associated information such as species, measurement conditions, growing conditions etc. <strong>- columns_descriptions_units</strong>: a description of all columns in sheet 'data' as well as associated units (if applicable). <strong>- references</strong>: literature references of the data. Column 'refkey' can be used for cross-referencing with column 'refkey' in sheet 'data'. <strong>- references_methods</strong>: literature references for measurement methods of gm. Column 'refkey' can be used for cross-referencing with column 'method_reference' in sheet 'data'. <strong>- references_rubisco_parameters</strong>: literature references for rubisco parameters used in the studies. Column 'refkey' can be used for cross-referencing with columns 'Rubisco_constants_Ci_reference' and 'Rubisco_constants_Cc_reference' in sheet 'data'. The xlsx file can be imported into software environments such as R or python for further analysis. To read into R (tested for R version 4.1.2), a package such as <em>readxl</em> needs to be installed and loaded first, after which individual tabs can be imported using the <em>read_xlsx() </em>function. In python, the file can be imported using the <em>pandas.read_excel </em>command available from the pandas package. <br> The file <strong>aggregate_by_method.R </strong>provides code to read the xlsx dataset and aggregate by measurement method as described in the reference above. <br> For questions or comments please contact Dr. Jürgen Knauer (J.Knauer@westernsydney.edu.au). <br>
DOI: 10.6084/m9.figshare.26731975.v1
title: Additional file 3 of Rating of Perceived Exertion: A Large Cross-Sectional Study Defining Intensity Levels for Individual Physical Activity Recommendations

description: R-Session Information.
DOI: 10.15468/dl.8qmxyp
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Petalostemon purpureus (Vent.) Rydb.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 24 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013099-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/swtd9m4gsf
title: Data for: A fuzzy logic based soil chemical quality index for cacao

description: The soil chemical quality index data.
DOI: 10.6084/m9.figshare.12595019.v1
title: BCA Bootstrap in MS EXCEL

description: Data were collected through an online survey and processed to create 95% CI using the BCA bootstrap confidence interval algorithm in MS EXCEL. Construction of confidence interval in MS EXCEL using the BCA bootstrap confidence interval algorithm is earlier not presented in any studies. The macro capabilities of MS EXCEL was utilized for the purpose stated.
DOI: 10.6084/m9.figshare.21408291.v2
title: CIED-Emission factors, 2015-2018

description: The Chinese Industrial Emissions Database (CIED) provides inventory of particulate matter (PM), SO2 and NOX from Chinese industries from 2015 to 2018, based on direct measurements from China’s continuous emission monitoring systems (CEMS) network. This document provides emissions factors for China’s different industrial emission sources.
DOI: 10.6084/m9.figshare.26894011.v1
title: Supplementary code and datasets for eDL-cSIM

description: We provide the open-source code and associated dataset of eDL-cSIM.
DOI: 10.17632/879xmdz3d8
title: Code Examples for Statistical Modeling and Robust Inference for One-shot Devices, Balakrishnan and Castilla

description: Code Examples from the Book
DOI: 10.6084/m9.figshare.12417251.v1
title: DISPERSE, a trait database to assess the dispersal potential of European aquatic macroinvertebrates

description: DISPERSE includes 39 trait categories grouped into nine dispersal-related traits for 480 taxa, including Annelida, Mollusca, Platyhelminthes, and Arthropoda such as Crustacea and Insecta, generally at the genus level
DOI: 10.6084/m9.figshare.1491375.v1
title: GIS Research UK (GISRUK) 2015 Proceedings

description: This volume contains the papers presented at GIS Research UK 2015 (GISRUK2015) held at the School of Geography, University of Leeds, on 15-17 April 2015.
DOI: 10.6084/m9.figshare.21755345.v1
title: Functional group analyses of herpetofauna in South Korea using a large dataset

description: ‘Data file 1_Functional trait amphibians’ and ‘Data file 2_Functional trait reptiles’ are functional trait database. <br> ‘Data file 3_SpeciesWgrid_Fun.Group’ is the species occurrence and functional group data of each grid. <br> ‘Data file 4_Diversity indices distribution’ is shapefile and shows the average functional diversity indices and species richness on each grid.
DOI: 10.6084/m9.figshare.8166815.v1
title: Genetic sequences of coding sequences found in kps loci.

description: Two FASTA files are provided, both compressed to *tar.gz format: kps.cds.nt.fa lists all nucleotide sequences of coding sequences while kps.cds.aa.fa lists translated (and frame-shift corrected) sequences of the same coding sequences. Each FASTA entry has the following syntax: A__B, where A is the name of the RefSeq accession entry (with the corresponding version after a dot), and B is the identified gene name.
DOI: 10.17632/nbd4r5h29y
title: The value of hospital dermatology consultation in improving patient outcomes through expert management: A retrospective review of hospital dermatology consultations at a tertiary academic institution

description: The supplemental data provides the reader with additional information on data collection and specific management changes after dermatologic consultation. 

Mendeley Supplemental Table 1: Adjustments in treatment post-dermatology consultation 

Mendeley Supplemental Figure 1: Flow chart description of data collection and definition of variables 
Legend:
*Primary categories include drug eruption, infection, primary cutaneous dermatitis, manifestation of systemic disease, or primary cutaneous malignancy
**Secondary sub-categories provide further description of primary (e.g. fungal v. bacterial infection)
***Modalities include topical or systemic
****Mechanism of action describes pharmacologic method of therapeutic effect
DOI: 10.17632/sr279sf4hs
title: CocoaBeansQCV

description: Data for publication in Data in Brief. Cocoa Beans Quality dataset applies an image processing approach in Eight Steps. Every step implements a Computer Vision Algorithm in two condition of object classification, namely good and poor-quality cocoa beans. This dataset is already used in cocoa bean quality identification in image processing studies, so it is available for any purposes for the next project. Acquisition of the data using Video Capture and Extraction into raw image. Data was obtained from captured video using a data acquisition box device designed to connect to a conveyor. The image processing step to evaluate was based on the pre-processing approach with resizing, sharpening, RGB2HSV, Masking, Bounding Box, and Cropping Object.
DOI: 10.6084/m9.figshare.26827948.v1
title: CldNetV2 - 202201

description: Retrieval of all-day cloud property based on satellite remote sensing dataGithub page: https://github.com/metdai/PMD-CldNetV2
DOI: 10.17632/nr8x6yxz2m
title: Data for: Antiferroelectric behavior and giant strain in BNKT complex tungsten bronze Cs2Nb4O11 ceramics
DOI: 10.6084/m9.figshare.11717670.v1
title: S1_XPS

description: X-ray photoelectron spectroscopy data acquired for epitaxial graphene on silicon carbide intercalated with Ga, In, or Sn.<br>"All Elements_O2HeEG_annealed at intercalation conditions" contains spectra for a sample of epitaxial graphene on silicon carbide (EG/SiC) that was exposed to a 50W O2/He plasma for 60s and subsequently annealed at 800C, 300 Torr for 30 minutes under 50 sccm Ar. <br>"All Elements_O2HeEG_SiC_HOPG_AsGrownEG_HydrogEG_GaIntO2HeEG_GaIntAsGrownEG" contains spectra for the following samples: O2/He plasma treated EG/SiC, bare SiC, highly ordered pyrolytic graphite (HOPG), as-grown EG/SiC, hydrogenated (or hydrogen intercalated) EG/SiC, Ga-intercalated, O2/He plasma treated EG/SiC, and Ga-intercalated as-grown EG/SiC. This file contains C 1s, O 1s, Si 2p, Ga 3d, and survey spectra.<br>"C1sEGGaInt" contains C 1s spectra for O2/He plasma treated EG/SiC, bare SiC, highly ordered pyrolytic graphite (HOPG), as-grown EG/SiC, hydrogenated (or hydrogen intercalated) EG/SiC, Ga-intercalated, O2/He plasma treated EG/SiC, and Ga-intercalated as-grown EG/SiC.<br>"Ga3dGaInt" contains Ga 3d spectra for Ga-intercalated, O2/He plasma treated EG/SiC, Ga-intercalated as-grown EG/SiC.<br>"InSnInterc" contains O 1s, Si 2p ,C 1s, Sn 3d and In 3d spectra for Sn and In-intercalated, O2/He plasma treated EG/SiC.<br>"O1s_GaIntO2HeEG_GaIntAsGrownEG_Hinterc" contains O 1s spectra for Ga-intercalated, O2/He plasma treated EG/SiC, Ga-intercalated as-grown EG/SiC.<br>"Si2pO2HeEGAsG" contains Si 2p spectra for O2/He plasma treated EG/SiC, as-grown EG/SiC, hydrogenated (or hydrogen intercalated) EG/SiC, Ga-intercalated, O2/He plasma treated EG/SiC, and Ga-intercalated as-grown EG/SiC.
DOI: 10.6084/m9.figshare.21082699.v1
title: Supplementary material 1.docx

description: We provide the suplementary table 1 of our article entitled "X-linked hypophosphatemia, not only a skeletal disease but also a chronic inflammatory state". We assessed inflammation among XLH patients in their blood cells and osteoclasts using RT-qPCR techniques. We provide here the primers used for the eight genes tested in our study.
DOI: 10.6084/m9.figshare.5890456.v1
title: network.net

description: This file contains information related to the structure of the co-authorship network.
DOI: 10.6084/m9.figshare.1597612.v3
title: Best Practices in Environmental Communication - Appendices

description: Appendices for: Jarreau, P. B., Altinay, Z., & Reynolds, A. (2015). Best Practices in Environmental Communication: A Case Study of Louisiana's Coastal Crisis.Environmental Communication, 1-23.
DOI: 10.6084/m9.figshare.9862586.v1
title: Macaque Faces

description: This dataset contains ~4000 photos of rhesus macaque faces. There are 81 individual rhesus macaques from two different colonies (A and B). There are also two spreadsheets containing information about the individual animals and the images. For the monkeys from colony A the photos are arranged by identity in a zipped file (for example Arlene.zip contains all the images of Arlene). For colony B all the images are in a single zip file (Threat Neutral Faces Pairs). The images spreadsheet contains information about which zip file each image file is stored in.
DOI: 10.17632/s8py5wdk4g
title: Ground Based SAR Data of Cardboard box with Aluminium Bottles for Anomaly Detection

description: The dataset consists of 155 raw Ground Based SAR (GBSAR) data examples for anomaly detection outside of visible area. Specifically, 5 aluminium bottles are set in the card box.
4 different scenes are recorded:
1. without anomaly (50 examples)
2. with anomaly placed instead of the first bottle (35 examples)
3. with anomaly placed instead of the third bottle (35 examples)
4. with anomaly placed instead of the fifth bottle (35 examples)

Each example is stored as matrix in .txt format. Matrix is size of 1024x24 (1024 frequency points, 24 GBSAR steps)

Raw data is obtained using developed GBSAR system. It works in stop-and-go mode with 1 cm step size and total aperture of 24 cm. GBSAR sensor is FMCW (Frequency Modulated Continuous Wave) radar with central frequency at 24 GHz and 1.3 GHz wide bandwidth. FMCW radar periodically transmits sawtooth signal every 166 ms. Wave polarization of FMCW radar is set to vertical.

This work was supported in part by Croatian Science Foundation (HRZZ) under the project number IP-2019-04-1064.
DOI: 10.6084/m9.figshare.11777697.v1
title: Papua New Guinea elevation data

description: Altitude data from SRTM downloaded from raster R package - stored here for markdown file.
DOI: 10.6084/m9.figshare.1552023.v1
title: Soybean Dataset

description: Soybean Dataset
DOI: 10.6084/m9.figshare.17903834.v3
title: Rat 3D Tracking & E-Phys KISN 2020 Dataset

description: This dataset was collected in 2020 in the Whitlock Lab at the Kavli Institute for Systems Neuroscience (KISN) in Norway by Bartul Mimica and Tuce Tombaz. In the "data_files" directory, each pickle file is a separate (~20 min) recording session of an individual rat moving freely in a 2x2 m octagonal arena, with seven 3D motion captured body points, head and back Euler angles derived from such tracked points, inertial measurement unit-derived head Euler angles, sound stimulus presentation, spiking activity etc. In the "cluster_group_identity_files" directory, one can find whether individual clusters were classified as single units ("good") or multi-unit activity ("mua"). Further details about data structure can be found in the "data_description.pdf" file.
DOI: 10.6084/m9.figshare.19068167.v1
title: Grazing experiments

description: Row data of propagule survival
DOI: 10.6084/m9.figshare.21175042.v3
title: Supplementary Material for: The mitochondrial genome of Isognomon nucleus and mitophylogenomics (Bivalvia: Autobranchia)

description: <strong>Appendix S1: </strong>Expanded Materials and Methods <strong>Appendix S2: </strong>Sequencing depth analysis <strong>Appendix S3: </strong>Lack of a convincing <em>ATP8</em> gene <strong>Appendix S4: </strong>Potential long novel transmembrane domain containing ORFs in <em>I. nucleus </em>and <em>P. fucata martensii</em> <strong>Fig. S1. </strong>Length versus base quality plot of ONT reads (within Appendix S1) <strong>Fig. S2. </strong>Evidence for a small gap in the <em>I. nucleus </em>mitogenome from ONT-only assembly (within Appendix S1) <strong>Fig. S3. </strong>Distribution of observed and theoretical protein sizes from ORFs found in the unannotated portion of the <em>I. nucleus </em>mitogenome (within Appendix S4) <strong>Table S1:</strong> Structural features of the standard mitochondrial genes in the <em>Isognomon nucleus </em>mitogenome <strong>Table S2:</strong> Summary statistics for ONT reads via NanoStat (within Appendix S1) <strong>Table S3:</strong> List of species, accession numbers and mitogenome sizes used in this study (within Appendix S1) <br> <strong>Data S1. </strong>Sequences of the 4 long <em>Isognomon nucleus </em>proteins with multiple predicted transmembrane domains (these were included in the official annotation of the mitogenome) <strong>Data S2.</strong> Transmembrane domain identification for the <em>I. nucleus </em>predicted proteins by DeepTMHMM (gff output format) <strong>Data S3.</strong> Sequences of the 5 long <em>Pinctada fucata martensii</em> proteins with multiple predicted transmembrane domains <strong>Data S4.</strong> Transmembrane domain identification for the <em>P. fucata martensii</em> predicted proteins by DeepTMHMM (gff output format) <strong>Sup. Data 5.</strong> <em>(Not in this file; available at Figshare)</em> Contains the curated, GBLOCKS-alignment, all files from the IQTREE run and pdf exports of the estimated ML tree from FigTree. [https://figshare.com/projects/Isognomon_mitogenomes_Supplementary_Data_Files_tables/129485] <strong>Sup. Data 6.</strong> Sequences of the 3 additional long <em>Isognomon nucleus </em>proteins with multiple predicted transmembrane domains (these were not included in the official mitogenome annotation)
DOI: 10.6084/m9.figshare.21608472.v1
title: Supplementary Table 3: Microbiota richness and diversity (Observed richness, Shannon diversity, Evenness).

description: Microbiota richness and diversity (Observed richness, Shannon diversity, Evenness).
DOI: 10.6084/m9.figshare.28938005.v4
title: SMT_ImageSet

description: This dataset is designed for research and development in surface-mount technology (SMT) image enhancement, defect detection, and visual recognition under varying illumination and imaging conditions.
DOI: 10.6084/m9.figshare.9956444.v1
title: bacterial GWAS benchmark simulations: lowLD & Sample size 3000

description: Supplementary data for the paper "Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes": https://www.biorxiv.org/content/10.1101/795492v1
DOI: 10.6084/m9.figshare.13241972.v10
title: BUDDY. A database to compare life history strategies across ectotherms.

description: <strong>What is BUDDY about?</strong> How can we predict how populations respond to the ever greater changes in their environment? Within this project, we want to know which characteristics of organisms relate to population responses to environmental change. One way to find out is to analyse life history patterns using demographic models. However, depending on whether you model individual life histories from phenomenological descriptions (Salguero-Gomez et al. 2016; Paniw et al. 2018; Capdevila et al 2020) or from mechanistic descriptions using energy budget models (Smallegange et al. in press; Smallegange & Berg 2019), different predictions are obtained. <br> This project aims to (i) unravel if energy budget descriptions of individual life histories consistently return different predictions on population responses to environmental change compared to when individual life histories are represented by statistical functions, (ii) understand why that is the case, and (iii) identify the most accurate way to predict population responses to novel environmental change. To this end, we support Bachelor, Master and PhD student projects in which students answer their own research questions, while at the same time expanding the DEB-IPM database to ultimately conduct large, cross-taxonomical life history analyses.