cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24540961
title: Database Education 5.0 091123

description: Education 5.0 is nowadays a new field to enhance natural and complex thinking, rooted in personalized, autonomous, and technology-enhanced learning experiences, but the current horizons for sustainability of this new posture are not well identified in the literature.
DOI: 10.25582/data.2022-07.2566990/2202172
title: Data for EMSL Project 60242 from July 2022
DOI: 10.25582/data.2022-07.2566349/2202173
title: Data for EMSL Project 60242 from July 2022
DOI: 10.25582/data.2022-07.2566963/2202174
title: Data for EMSL Project 60242 from July 2022
DOI: 10.25582/data.2022-08.2571660/2202175
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569391/2202176
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569388/2202177
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-06.2547546/2202178
title: Data for EMSL Project 60251 from June 2022
DOI: 10.25582/data.2022-08.2569381/2202179
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-09.2595224/2202180
title: Data for EMSL Project 60251 from September 2022
DOI: 10.25582/data.2022-08.2571720/2202181
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2548431/2202182
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2548082/2202183
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2571694/2202184
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-09.2595222/2202185
title: Data for EMSL Project 60251 from September 2022
DOI: 10.25582/data.2022-08.2569396/2202186
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-09.2595223/2202187
title: Data for EMSL Project 60251 from September 2022
DOI: 10.25582/data.2022-07.2547650/2202188
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547828/2202189
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547788/2202190
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2570470/2202191
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2570471/2202192
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547681/2202193
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547785/2202194
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547948/2202195
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569387/2202196
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-09.2595365/2202197
title: Data for EMSL Project 60251 from September 2022
DOI: 10.25582/data.2022-07.2547613/2202198
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569384/2202199
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569393/2202200
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547678/2202201
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569392/2202202
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569395/2202203
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2571651/2202204
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547580/2202205
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569379/2202206
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569386/2202207
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2548507/2202208
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-09.2595427/2202209
title: Data for EMSL Project 60251 from September 2022
DOI: 10.25582/data.2022-07.2547712/2202210
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569397/2202211
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2570473/2202212
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547585/2202213
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547619/2202214
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2570472/2202215
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2571724/2202216
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2571700/2202217
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547716/2202218
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547554/2202219
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569380/2202220
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-07.2547835/2202221
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-07.2547645/2202222
title: Data for EMSL Project 60251 from July 2022
DOI: 10.25582/data.2022-08.2569383/2202223
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2570451/2202224
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-08.2569385/2202225
title: Data for EMSL Project 60251 from August 2022
DOI: 10.25582/data.2022-01.2413971/2202226
title: Data for EMSL Project 60243 from January 2022
DOI: 10.25582/data.2022-08.2588863/2202227
title: Data for EMSL Project 60243 from August 2022
DOI: 10.25582/data.2022-08.2574530/2202228
title: Data for EMSL Project 60243 from August 2022
DOI: 10.25582/data.2022-04.2503985/2202229
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503991/2202230
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503992/2202231
title: Data for EMSL Project 60253 from April 2022
DOI: 10.6084/m9.figshare.24223114
title: Tabla Suplementaria 5, Artículo 3, y Tablas Suplementarias Excel 1 y 2

description: Material suplementario tesis Lara Sanoguera Miralles.
DOI: 10.6084/m9.figshare.24223114.v1
title: Tabla Suplementaria 5, Artículo 3, y Tablas Suplementarias Excel 1 y 2

description: Material suplementario tesis Lara Sanoguera Miralles.
DOI: 10.25582/data.2022-02.2439048/2202232
title: Data for EMSL Project 60253 from February 2022
DOI: 10.25582/data.2022-04.2503987/2202233
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-02.2441841/2202234
title: Data for EMSL Project 60253 from February 2022
DOI: 10.25582/data.2022-04.2504940/2202235
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503995/2202236
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503988/2202237
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-06.2546096/2202238
title: Data for EMSL Project 60253 from June 2022
DOI: 10.25582/data.2022-04.2503993/2202239
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-02.2439152/2202240
title: Data for EMSL Project 60253 from February 2022
DOI: 10.25582/data.2022-04.2503986/2202241
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503989/2202242
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-04.2503990/2202243
title: Data for EMSL Project 60253 from April 2022
DOI: 10.25582/data.2022-03.2461954/2202244
title: Data for EMSL Project 60250 from March 2022
DOI: 10.5439/2204329
title: G-band radar instantaneous Doppler spectra of clouds and light precipitation during the EPCAPE campaign in March and April, 2023

description: These files contain G-band radar instantaneous Doppler spectra of clouds and light precipitation in the form of calibrated reflectivity as a function of Doppler velocity and altitude. Data are available for several days from March 23 to April 27, 2023.
DOI: 10.5439/2204401
title: G-band radar Doppler spectra moments of clouds and light precipitation during the EPCAPE campaign in March and April, 2023

description: These files contain G-band radar Doppler spectra moments of clouds and light precipitation, i.e. calibrated reflectivity, mean Doppler velocity and Doppler spectrum width, organized by altitude and time. Data are available for several days from March 23 to April 27, 2023.
DOI: 10.5439/2204402
title: Ka, W and G-band radar measurements of clouds and light precipitation during the EPCAPE campaign in March and April, 2023

description: These files contain Ka, W and G-band radar measurements of clouds and light precipitation, organized by altitude and time. The files include calibrated reflectivity and dual-frequency reflectivity ratios. Data are available for several days from March 23 to April 27, 2023.
DOI: 10.6084/m9.figshare.24540979
title: Qualimap Reports, BAM QC

description: Qualimap Reports, BAM QC
DOI: 10.6084/m9.figshare.24540979.v1
title: Qualimap Reports, BAM QC

description: Qualimap Reports, BAM QC
DOI: 10.6084/m9.figshare.24540928
title: Validation of global precipitation time series products against tree ring records and remotely sensed vegetation greenness - Dataset and R scripts

description: The files in this collection include (1) EVI data (area under the curve) aggregated to 5km from MODIS collection MCD12Q2 v006 used for validation of climate data; (2) Climate data for seven global climate data products corresponding to EVI aggregated data; (3) Linear model results for each individual climate product and script used to generate those results; (4) Detrended tree ring chronology data from the International Tree Ring Data Bank; (5) Climate data for the seven global climate data products extracted for each chronology site; (6) Results from linear models applied to moving windows of the chronology data; and (7) the complete workflow to process climate data rasters, EVI rasters and tree ring chronologies to produce file numbered 1-6.
DOI: 10.6084/m9.figshare.24541006
title: Supplementary Material for: Clinical features, treatment modalities and outcomes of elderly thymoma patients: A propensity–matched study based on the SEER database

description: Introduction: Thymoma is a common mediastinal tumor, but few studies have been performed in thymoma patients 80 years or older. This study aimed to analyze the clinical features, treatment modalities and survival outcomes of thymoma patients at least 80 years old and compare these features to those of patients younger than 80 years old. Method: Data from thymoma patients in the Surveillance, Epidemiology and End Results database between 2000 and 2019 were selected. Clinical features, treatment modalities of the two age groups were compared. Survival rates were calculated by the Kaplan-Meier method and the log-rank test was used to compare survival rates between two groups. Propensity score matching was used based on whether surgery was performed. Univariate and multivariate Cox proportional–hazards regression analyses were performed to identify independent prognostic factors. Results: Compared with the younger patients, the patients aged 80 years or older had a similar distribution of Masaoka–Koga tumor stage, a higher proportion of type A thymoma, and a lower recurrence rate in the early stage. In elderly patients after propensity score matching, the overall survival and cancer–specific survival were better in the surgery group with complete resection and similar benefit from surgery as younger patients were observed. Conclusion: In thymoma patients aged 80 years or older, surgery still plays an important role in survival outcome. Compared with younger patients, older patients had unique clinical features.
DOI: 10.6084/m9.figshare.24541006.v1
title: Supplementary Material for: Clinical features, treatment modalities and outcomes of elderly thymoma patients: A propensity–matched study based on the SEER database

description: Introduction: Thymoma is a common mediastinal tumor, but few studies have been performed in thymoma patients 80 years or older. This study aimed to analyze the clinical features, treatment modalities and survival outcomes of thymoma patients at least 80 years old and compare these features to those of patients younger than 80 years old. Method: Data from thymoma patients in the Surveillance, Epidemiology and End Results database between 2000 and 2019 were selected. Clinical features, treatment modalities of the two age groups were compared. Survival rates were calculated by the Kaplan-Meier method and the log-rank test was used to compare survival rates between two groups. Propensity score matching was used based on whether surgery was performed. Univariate and multivariate Cox proportional–hazards regression analyses were performed to identify independent prognostic factors. Results: Compared with the younger patients, the patients aged 80 years or older had a similar distribution of Masaoka–Koga tumor stage, a higher proportion of type A thymoma, and a lower recurrence rate in the early stage. In elderly patients after propensity score matching, the overall survival and cancer–specific survival were better in the surgery group with complete resection and similar benefit from surgery as younger patients were observed. Conclusion: In thymoma patients aged 80 years or older, surgery still plays an important role in survival outcome. Compared with younger patients, older patients had unique clinical features.
DOI: 10.5281/zenodo.10099352
title: stats blue-bases
DOI: 10.5281/zenodo.10099353
title: stats blue-bases
DOI: 10.6084/m9.figshare.24541021
title: Qualimap Reports, BAM QC

description: These data were collected to perform a detailed genomic characterization of <i>Mycobacterium tuberculosis</i> strains isolated from pre-extensively drug-resistant tuberculosis patients in Kazakhstan.
DOI: 10.6084/m9.figshare.24541021.v1
title: Qualimap Reports, BAM QC

description: These data were collected to perform a detailed genomic characterization of <i>Mycobacterium tuberculosis</i> strains isolated from pre-extensively drug-resistant tuberculosis patients in Kazakhstan.
DOI: 10.5061/dryad.92rv6bq/1
title: FairyWrenData

description: The aim here is to quantify the effect of the group size an individual experiences in a given year on an integrative measure of fitness in two closely related fairy-wrens (Malurus elegans and M. cyaneus). Here, we combine six separate fitness components into one aggregate fitness proxy, using the concept of reproductive value. RV describes the expected contribution by individuals of a given age- or stage-class (here group size, sex and social status) to the future long-term growth of a population. We determined six fitness components: group productivity (number of fledglings produced per group per breeding season); within-group parentage (probability of paternity/maternity within own group); extra-group paternity (number of offspring sired by male in other social group); offspring sex-ratio; juvenile and adult survival; probability of obtaining dominant status; and the probability of increasing/decreasing the group size in the next year. Reproduction was split up into a within- and extra-group parentage component, as group members can parasitize the reproductive success of the dominant breeding pair. These six fitness components were integrated using reproductive values (RVs). RVs were calculated from population matrices, which were derived from a life cycle model. The next step was then to translate our life-cycle graph into a matrix population model, in which the matrix elements consist of the six fitness components in which we are interested. We used the statistical model estimates of each fitness component derived from the field data as input to the projection matrix, which allows—using standard matrix algebra— calculating the RVs of individuals as a function of their status, sex and group size.
DOI: 10.5281/zenodo.3405239
title: Congenic genotypes

description: “Congenic genotypes” This table shows sequenom generated SNP genotypes across the genomes of SHR-A3 and the SHR-A3(<em>IgH</em>-B2) congenic lines. There are two columns of genotypes for two individual SHR-A3(<em>IgH</em>-B2) congenics. These are both progeny of the same founders. All their genotypes outside of the <em>IgH</em>locus are identical to SHR-A3. The list includes two genotypes targeting the proximal and distal ends of the <em>IgH</em>locus. These were typed by PCR and gel analysis. Both are SHR-B2 alleles.
DOI: 10.5281/zenodo.3405240
title: Congenic genotypes

description: “Congenic genotypes” This table shows sequenom generated SNP genotypes across the genomes of SHR-A3 and the SHR-A3(<em>IgH</em>-B2) congenic lines. There are two columns of genotypes for two individual SHR-A3(<em>IgH</em>-B2) congenics. These are both progeny of the same founders. All their genotypes outside of the <em>IgH</em>locus are identical to SHR-A3. The list includes two genotypes targeting the proximal and distal ends of the <em>IgH</em>locus. These were typed by PCR and gel analysis. Both are SHR-B2 alleles.
DOI: 10.5281/zenodo.3405226
title: PsPM-RRM1-2: SCR, ECG and respiration measurement in response to electric stimulation or visual targets

description: This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements for each of 29 healthy unmedicated participants (7 males and 22 females aged 23.5 +/- 3.6 years) in response to 10 discomforting electric stimulations to the forearm (RRM1) or 10 visual targets in a visual detection task (RRM2). The sample partly overlaps with data set PsPM-FR. Some participants did not take part in RRM1 or RRM2 such that there are 25 recordings for RRM1 and 26 recordings for RRM2. Electric shock stimuli are 0.2 ms wide square current pulse repeated at 500 Hz for 500 ms and individually adjusted amplitude just below the pain threshold. Visual stimuli are red crosses (+) embedded in a white digit stream; each stimulus is presented during 200 ms and separated by a 800 ms blank interval. ITI is selected randomly on each trial from 40 s, 45 s or 50 s. A baseline period with distractors but no targets concludes experiment RRM2. (This is in contrast to the methods description in Bach et al. (2016), according to which the baseline period was randomly either in the beginning or at the end of the experiment. This discrepancy was caused by an error in the code that controlled the experiment presentation.)
description: See the readme file for more detail.
DOI: 10.5519/qd.r2fzswyr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10423 records)
DOI: 10.5061/dryad.5bh0587/1
title: Dryad Item 10.5061/DRYAD.5BH0587/1

description: (:tba)
DOI: 10.5061/dryad.fb01pj3/1
title: Dryad Item 10.5061/DRYAD.FB01PJ3/1

description: (:tba)
DOI: 10.5061/dryad.fb01pj3/2
title: Dryad Item 10.5061/DRYAD.FB01PJ3/2

description: (:tba)
DOI: 10.7910/dvn/ofqu9j/311kkw
title: Terrestrial.dbf

description: :unav
DOI: 10.7910/dvn/ofqu9j/lh34pu
title: Terrestrial.shp

description: :unav
DOI: 10.7910/dvn/ofqu9j/prgtv1
title: Marine.dbf

description: :unav
DOI: 10.7910/dvn/ofqu9j/sxmn3r
title: Marine.shp

description: :unav
DOI: 10.7910/dvn/ofqu9j/y77rix
title: Terrestrial.prj

description: :unav
DOI: 10.7910/dvn/vxpuk8/j5mstc
title: Source_List.docx

description: List of sources used in data tables and ZIP file
DOI: 10.7910/dvn/ofqu9j/dg3l4p
title: GMSB_README.pdf

description: :unav
DOI: 10.7910/dvn/ofqu9j/n6ten0
title: Terrestrial.shx

description: :unav
DOI: 10.7910/dvn/ofqu9j/wjlqta
title: Marine.shx

description: :unav
DOI: 10.7910/dvn/vxpuk8/9ifrwv
title: Studied_Caves.xlsx

description: Raw Data associated with studied caves
DOI: 10.7910/dvn/vxpuk8/c2dmt4
title: Source_List.docx

description: List of sources used in data tables
DOI: 10.7910/dvn/vxpuk8/kgmqij
title: Unstudied_Caves.tab

description: Raw Data associated with unstudied caves
DOI: 10.7910/dvn/vxpuk8/m93g4x
title: Geologic_Map.zip

description: Shapefile for the areas that had potential areas identified. Data taken from: Hartmann, J. and Moosdorf, N., (2012) The new global lithological map database GLiM: A representation of rock properties at the Earth surface: TECHNICAL BRIEF. Geochemistry, Geophysics, Geosystems, [online] 1312. Available at: http://doi.wiley.com/10.1029/2012GC004370 [Accessed 24 Jul. 2019]. Natural Earth, (2018a) Coastline. [online] Natural Earth Data. Available at: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/. Natural Earth, (2018b) Land. [online] Natural Earth Data. Available at: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-land/.
DOI: 10.5061/dryad.rr4g39b/1
title: Dryad Item 10.5061/DRYAD.RR4G39B/1

description: (:tba)
DOI: 10.5281/zenodo.3405250
title: reactome2compartments

description: Creates pathway compartment definitions from Reactome data ( https://reactome.org/ ) and creates a compartment to gene group definition file (gmt). https://github.com/richardtjornhammar/reactome2compartments
DOI: 10.5281/zenodo.3405251
title: reactome2compartments

description: Creates pathway compartment definitions from Reactome data ( https://reactome.org/ ) and creates a compartment to gene group definition file (gmt). https://github.com/richardtjornhammar/reactome2compartments
DOI: 10.17632/ckync9yvvr.1
title: Dataset for the metamodeling of naturally ventilated Brazilian low-cost houses to assess thermal performance

description: Research objective: This study aimed to develop metamodels to assess the thermal discomfort in naturally ventilated Brazilian low-cost houses during early design as a decision-making support framework, and for educational purposes. Method overview: The method encompassed a large number of simulations of the software EnergyPlus [EP] 8.1 using the Monte Carlo method. These simulations were used to develop a set of regression-based mathematical relationships between the inputs and the outputs. The Monte Carlo method was selected to help sample the many independent input variables, each with its own range of values, and form equally likely random combinations of input conditions for the energy simulation. The EnergyPlus outputs were post processed to assess the thermal comfort by means of the degree hours of discomfort by heat and by cold. Files description: The following data items were made available. (a) Parameter_Domains: Curitiba_parameterdomains.csv; Manaus_parameterdomains.csv and Sao_Paulo_parametersdomains.csv: CSV files created for each location. They contain a list of the 24 key parameters and random combinations of their values to create the input data for the 10,000 simulations. (b) Performance_Metrics: Curitiba_performancemetrics.csv; Manaus_performancemetrics.csv and Sao_Paulo_performancemetrics.csv: CSV files created for each location. They contain output values (outdoor and indoor discomfort by heat and by cold) for 10,000 simulations. (c) Sandbox: Sandbox.xlsx: Excel file for the application of the metamodels. It enables a quick and easy assessment of discomfort by heat and by cold for specific combinations of parameters, for each location. (d) Python_Codes: Python_Codes.zip: Compressed file consisting of the codes used to 1) run the simulations randomly combining values for each selected parameter within their specified ranges, and 2) to calculate the hours of discomfort by heat and by cold for each of the parameters’ combinations (10,000 simulations for each studied climate). (e) IDFs_Base: Curitiba_idfbase.idf; Manaus_idfbase.idf; Sao_Paulo_idfbase.idf : Input Data File (IDF) created for each location. They contain the description of all input data considered in an annual building performance simulation.
DOI: 10.17632/327mwms9n2.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB94T section 1-2)