cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20486310
title: Global monthly averaged TDS, BOD and FC concentrations between 2021 and 2030 at 10km spatial resolution for 5 GCMs under two wastewater treatment scenarios.

description: Global 10km spatial resolution dataset for modelled total dissolved solids (TDS; mg/L), biological oxygen demand (BOD; mg/L) and fecal coliform (FC; cfu/100ml) averaged per month between 2021 and 2030 for 5 GCMs (GFDL, IPSL, MPI, MRI and UKESM) and under two wastewater treatment scenarios (no expansions in wastewater treatment vs. SDG6.3 attainment). <br> Model simulations are made using the Dynamic Water Quality Model (DynQual). Concentrations only calculated when (gridded) channel storage &gt;10m<sup>3</sup>.
DOI: 10.6084/m9.figshare.20486310.v1
title: Global monthly averaged TDS, BOD and FC concentrations between 2021 and 2030 at 10km spatial resolution for 5 GCMs under two wastewater treatment scenarios.

description: Global 10km spatial resolution dataset for modelled total dissolved solids (TDS; mg/L), biological oxygen demand (BOD; mg/L) and fecal coliform (FC; cfu/100ml) averaged per month between 2021 and 2030 for 5 GCMs (GFDL, IPSL, MPI, MRI and UKESM) and under two wastewater treatment scenarios (no expansions in wastewater treatment vs. SDG6.3 attainment). <br> Model simulations are made using the Dynamic Water Quality Model (DynQual). Concentrations only calculated when (gridded) channel storage &gt;10m<sup>3</sup>.
DOI: 10.5281/zenodo.7902675
title: VOYAGE: A Large Collection of Vocabulary Usage in Open RDF Datasets

description: <strong>List of files:</strong> odps.json: for each of the accessed ODPs, its name, URL, API type, API URL, and the IDs of RDF datasets collected from it JSON structure: a list of objects, where each object contains the following attributes - 'name' (string), 'URL' (string), 'API type' (string), 'API URL' (string), and 'collected datasets IDs' (list of integers) datasets.json: for each of the crawled RDF datasets, its ID, title, description, author, license, dump file URLs, and PLDs JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer), 'title' (string), 'description' (string), 'author' (string), 'license' (string), 'dump file URLs' (list of strings), and 'PLDs' (list of strings) deduplicated_datasets.json: the IDs of the deduplicated RDF datasets and whether they are in the LOD Cloud JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer) and 'in LOD Cloud' (boolean) terms.json: the extracted classes, properties, and the IDs of RDF datasets using each term JSON structure: a list of objects, where each object contains the following attributes - 'term' (string), 'is class' (boolean), 'is property' (boolean), and 'used in dataset IDs' (list of integers) vocabularies.json: the extracted vocabularies, the classes and properties in each vocabulary, and the IDs of RDF datasets using each vocabulary JSON structure: a list of objects, where each object contains the following attributes - 'vocabulary' (string), 'classes' (list of strings), 'properties' (list of strings), and 'used in dataset IDs' (list of integers). edps.json: the extracted distinct EDPs and the IDs of RDF datasets using each EDP JSON structure: a list of objects, where each object contains the following attributes - 'classes' (list of strings), 'forward properties' (list of strings), 'backward properties' (list of strings), and 'used in dataset IDs' (list of integers) clusters.json: the clusters of vocabularies generated by MV-ITCC and LDA JSON structure: {"LDA": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}}, "MV-ITCC": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}, "dataset IDs": {DATASET_CLUSTER_ID_1: [LIST_OF_DATASET_IDS], DATASET_CLUSTER_ID_2: [LIST_OF_DATASET_IDS], ...}}}
DOI: 10.5281/zenodo.7902674
title: VOYAGE: A Large Collection of Vocabulary Usage in Open RDF Datasets

description: <strong>List of files:</strong> odps.json: for each of the accessed ODPs, its name, URL, API type, API URL, and the IDs of RDF datasets collected from it JSON structure: a list of objects, where each object contains the following attributes - 'name' (string), 'URL' (string), 'API type' (string), 'API URL' (string), and 'collected datasets IDs' (list of integers) datasets.json: for each of the crawled RDF datasets, its ID, title, description, author, license, dump file URLs, and PLDs JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer), 'title' (string), 'description' (string), 'author' (string), 'license' (string), 'dump file URLs' (list of strings), and 'PLDs' (list of strings) deduplicated_datasets.json: the IDs of the deduplicated RDF datasets and whether they are in the LOD Cloud JSON structure: a list of objects, where each object contains the following attributes - 'ID' (integer) and 'in LOD Cloud' (boolean) terms.json: the extracted classes, properties, and the IDs of RDF datasets using each term JSON structure: a list of objects, where each object contains the following attributes - 'term' (string), 'is class' (boolean), 'is property' (boolean), and 'used in dataset IDs' (list of integers) vocabularies.json: the extracted vocabularies, the classes and properties in each vocabulary, and the IDs of RDF datasets using each vocabulary JSON structure: a list of objects, where each object contains the following attributes - 'vocabulary' (string), 'classes' (list of strings), 'properties' (list of strings), and 'used in dataset IDs' (list of integers). edps.json: the extracted distinct EDPs and the IDs of RDF datasets using each EDP JSON structure: a list of objects, where each object contains the following attributes - 'classes' (list of strings), 'forward properties' (list of strings), 'backward properties' (list of strings), and 'used in dataset IDs' (list of integers) clusters.json: the clusters of vocabularies generated by MV-ITCC and LDA JSON structure: {"LDA": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}}, "MV-ITCC": {"vocabularies": {VOCABULARY_CLUSTER_ID_1: [LIST_OF_VOCABULARIES], VOCABULARY_CLUSTER_ID_2: [LIST_OF_VOCABULARIES], ...}, "dataset IDs": {DATASET_CLUSTER_ID_1: [LIST_OF_DATASET_IDS], DATASET_CLUSTER_ID_2: [LIST_OF_DATASET_IDS], ...}}}
DOI: 10.6084/m9.figshare.22650748.v1
title: BC_cardiotox: A cardiotoxicity dataset for breast cancer patients

description: The BC_cardiotox database is a result of the collaboration between the University of A Coruña and the University Hospital of A Coruña. It contains information about 531 breast cancer patients and aims to enable the scientific community in conducting new research on cancer therapy-related cardiac dysfunction (CTRCD). The development of this side effect has important clinical and prognostic implications, and the availability of good predictors may allow its early detection. Although some risk factors are known, they are not sufficient to predict which patients will develop CTRCD during treatment. Therefore, in clinical practice it is recommended to monitor the appearance of CTRCD through blood laboratory tests and cardiac imaging tests. The echocardiogram, which allows to assess various parameters of the structure and function of the heart, is the main diagnostic tool. The Tissue Doppler Imaging (TDI), which shows the velocity of contraction and relaxation of the cardiac muscle, can be obtained by using this technique. This information, measured before treatment, may help in the early identification of CTRCD. Since the TDI shows a velocity as a function of time, it can be preprocessed to obtain a functional datum. <br> For each patient, baseline clinical variables related to anthropometric and heart measures are available, as well as the preprocessed baseline TDI. The code for the preprocessing of image data is also provided, together with two TDI images for reproducibility issues. The code is implemented in the statistical software R. <br> Corresponding author: Beatriz Piñeiro-Lamas (b.pineiro.lamas@udc.es / b.pineiro.lamas@gmail.com)
DOI: 10.5281/zenodo.7907282
title: SCALE-WIN19 MLD and Euphotic Depth

description: The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m<sup>-3</sup> from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m<sup>-1</sup>) calculated as the slope of the depth vs log(iPAR).
DOI: 10.5281/zenodo.7907281
title: SCALE-WIN19 MLD and Euphotic Depth

description: The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m<sup>-3</sup> from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m<sup>-1</sup>) calculated as the slope of the depth vs log(iPAR).
DOI: 10.5281/zenodo.7907290
title: SCALE-SPR19 MLD and Euphotic Depth

description: The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m<sup>-3</sup> from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m<sup>-1</sup>) calculated as the slope of the depth vs log(iPAR).
DOI: 10.5281/zenodo.7907289
title: SCALE-SPR19 MLD and Euphotic Depth

description: The file includes the mixed layer depth (MLD; m) calculated as: 1. Depth at which density changes by 0.03 kg m<sup>-3</sup> from the value at 10m 2. Depth of the maximum Brunt-Vaissala Frequency The file includes the euphotic depth (m) calculated as the depth at which iPAR is 1% of surface iPAR. Please note that not all stations have a value due to either being sampled at night or the depth of the CTD cast was deeper then depth rating of the sensor (1500 m). Included is the diffuse attenuation coefficient (m<sup>-1</sup>) calculated as the slope of the depth vs log(iPAR).
DOI: 10.11588/data/dunn8c/33wm2t
title: Screening_on_Human_Trafficking_Data_and_Analysis.tab

description: :unav
DOI: 10.11588/data/dunn8c/7ipadw
title: Countries_of_Origin_Screening_for_Human_Trafficking.PNG

description: :unav
DOI: 10.57967/hf/0622
title: dlgs_81_08_qa
DOI: 10.57967/hf/0434
title: dlgs_81_08_qa
DOI: 10.34740/kaggle/dsv/5632199
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.34740/kaggle/dsv/5632200
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.22778735
title: <strong>The Cost of Wider Fire Access Codes (Supplementary Material)</strong>

description: There are 4 files which supplement my manuscript. Excel File <em>Raw Data</em> contains elements including street name, street width, dwelling units, neighborhood area, and parking characteristics. <br> Neighborhood averages were copied to the Excel file <em>Measurement Summary</em> for additional analysis. <br> More detailed statistical calculations were performed using Texas Instruments TI-nspire software. Outputs are available in the PDF <em>Statistical Calculations</em>. <br> Following review, the paper’s methodology was simplified. The simplified methodology produces the same result as the original process, and the older calculation files were not modified. The Excel file <em>Updated Street Width Formulas</em> displays both processes side by side.
DOI: 10.5281/zenodo.7907455
title: Dataset for the Indonesian Interprofessional Socialisation and Valuing Scale (ISVS)-19

description: The dataset is used for the analysis of an article titled: Psychometric evaluation of the culturally adapted Interprofessional Socialisation and Valuing Scale (ISVS)-19 for health practitioners and students in Indonesia.
DOI: 10.5281/zenodo.7907454
title: Dataset for the Indonesian Interprofessional Socialisation and Valuing Scale (ISVS)-19

description: The dataset is used for the analysis of an article titled: Psychometric evaluation of the culturally adapted Interprofessional Socialisation and Valuing Scale (ISVS)-19 for health practitioners and students in Indonesia.
DOI: 10.25934/pr00003407.2
title: Available IPD datapackage for study 'PRoFESS - Prevention Regimen For Effectively Avoiding Second Strokes: A Double-blind, Active and Placebo Controlled Study of Aggrenox vs. Clopidogrel, With and Without Micardis'

description: The purpose of the trial is to determine if extended-release dipyridamole + aspirin [Aggrenox, Asasa ntin] is superior to clopidogrel [Plavix], and if telmisartan [Micardis, Gliosartan, Kinzal, Kinzalm ono, Predxal, Pritor, Samertan, Telmisartan] is superior to placebo, in the presence of background antihypertensive therapy, in prevention of a second stroke in patients who have recently suffered a stroke and therefore are at high risk of suffering another one.
DOI: 10.5281/zenodo.7568377
title: VEDSIB: VEhicular trajectory Dataset at Signalised1 Intersection in Bremen Dataset

description: VEDSIB dataset contains 26,007 trajectories recorded over 32 hours in a signalised intersection in Bremen, Germany. VEDSIB contains: Date: the date of the collected information Time: Time when the information was collected Direction: at the intersection, which direction the vehicle came from Number: the number of vehicles par minutes Type: The type of vehicles; either a car, a bus or a tram. Temperature in °F: the temperature observed at the time of collecting the information Humidity: humidity level Wind information (speed, Gust): the wind information Pressure, Precipitation, Condition: the general condition of the weather Rainy information: if there is rain or not at the time of collecting the data.
DOI: 10.5281/zenodo.7568376
title: VEDSIB: VEhicular trajectory Dataset at Signalised1 Intersection in Bremen Dataset

description: VEDSIB dataset contains 26,007 trajectories recorded over 32 hours in a signalised intersection in Bremen, Germany. VEDSIB contains: Date: the date of the collected information Time: Time when the information was collected Direction: at the intersection, which direction the vehicle came from Number: the number of vehicles par minutes Type: The type of vehicles; either a car, a bus or a tram. Temperature in °F: the temperature observed at the time of collecting the information Humidity: humidity level Wind information (speed, Gust): the wind information Pressure, Precipitation, Condition: the general condition of the weather Rainy information: if there is rain or not at the time of collecting the data.
DOI: 10.5281/zenodo.7896581
title: Bacteriorhodopsin TR-SFX input data for LPSA.

description: Input matrices for the low-pass spectral analysis of a time-resolved serial femtosecond crystallography data set from bacteriorhodopsin. T_sel_sparse_light.jbl: bR TR-SFX intensities (m Bragg points x S time points) ts_sel_light.jbl: associated timestamps in femtoseconds (S values) miller_h_light.jbl, miller_k_light.jbl and miller_l_light.jbl: m-element vectors with Miller indices. dT_bst_sparse_light_mirrored.jbl: intensity deviations derived from T_sel_sparse_light.jbl, by subtracting reflection-dependent time averages and applying a scale factor to account for the different redundancy of observations. To overcome the LPSA cyclic boundary conditions, 2S frames are generated, with frames S+1, ..., 2S corresponding to frames 1, ..., S in reversed order. ts_sel_light_mirrored: 2S timestamps associated to the frames in dT_bst_sparse_light_mirrored.jbl. Values 1, ..., S are the experimental timestamps in ts_sel_ligth.jbl, and values S+1, ..., 2S are fictitious. I_dark_avg.mtz: merged resting state data for map calculation.
DOI: 10.5281/zenodo.7896580
title: Bacteriorhodopsin TR-SFX input data for LPSA.

description: Input matrices for the low-pass spectral analysis of a time-resolved serial femtosecond crystallography data set from bacteriorhodopsin. T_sel_sparse_light.jbl: bR TR-SFX intensities (m Bragg points x S time points) ts_sel_light.jbl: associated timestamps in femtoseconds (S values) miller_h_light.jbl, miller_k_light.jbl and miller_l_light.jbl: m-element vectors with Miller indices. dT_bst_sparse_light_mirrored.jbl: intensity deviations derived from T_sel_sparse_light.jbl, by subtracting reflection-dependent time averages and applying a scale factor to account for the different redundancy of observations. To overcome the LPSA cyclic boundary conditions, 2S frames are generated, with frames S+1, ..., 2S corresponding to frames 1, ..., S in reversed order. ts_sel_light_mirrored: 2S timestamps associated to the frames in dT_bst_sparse_light_mirrored.jbl. Values 1, ..., S are the experimental timestamps in ts_sel_ligth.jbl, and values S+1, ..., 2S are fictitious. I_dark_avg.mtz: merged resting state data for map calculation.
DOI: 10.5281/zenodo.7907511
title: Shipments Sensors Readings (EPCIS)

description: Real-time sensor readings (including temperature, vibrations, humidity, pressure, position, speed, shock events etc.) for shipment of LL1 partners (COSCO,DHL.Citylogin)
DOI: 10.5281/zenodo.7907512
title: Shipments Sensors Readings (EPCIS)

description: Real-time sensor readings (including temperature, vibrations, humidity, pressure, position, speed, shock events etc.) for shipment of LL1 partners (COSCO,DHL.Citylogin)
DOI: 10.5281/zenodo.7907515
title: Shipments Sensors readings

description: Real-time sensor readings (including temperature, vibrations, humidity, pressure, position, speed, shock events etc.) for shipment of LL1 partners (COSCO,DHL.Citylogin)
DOI: 10.5281/zenodo.7907514
title: Shipments Sensors readings

description: Real-time sensor readings (including temperature, vibrations, humidity, pressure, position, speed, shock events etc.) for shipment of LL1 partners (COSCO,DHL.Citylogin)
DOI: 10.5281/zenodo.7876512
title: Warehouse Daily Volume Customer PLANET project

description: Warehouse Daily Volume Customer\
DOI: 10.5281/zenodo.7876511
title: Warehouse Daily Volume Customer PLANET project

description: Warehouse Daily Volume Customer\
DOI: 10.5281/zenodo.7876499
title: PI Cargo Network Cargo Flows

description: PI Cargo Network Cargo Flows
DOI: 10.5281/zenodo.7876498
title: PI Cargo Network Cargo Flows

description: PI Cargo Network Cargo Flows
DOI: 10.5281/zenodo.7876492
title: Origin-Destinace trade volumes between Europe and China

description: Origin-Destinace trade volumes between Europe and China
DOI: 10.5281/zenodo.7876491
title: Origin-Destinace trade volumes between Europe and China

description: Origin-Destinace trade volumes between Europe and China
DOI: 10.3217/mgd4n-gq267
title: A Thru-free Multiline Calibration: Dataset and Code

description: VNA wave parameter measurements of microstrip lines for demonstration of the thru-free multiline  calibration method.
DOI: 10.5281/zenodo.7907587
title: Parkinson disease validation algorithms

description: The primary objective of this study was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity 94.2% (95% CI 88.4 – 97.6) and the other with greater specificity 98.1% (95% CI 97.7 – 98.5).
DOI: 10.5281/zenodo.7907586
title: Parkinson disease validation algorithms

description: The primary objective of this study was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity 94.2% (95% CI 88.4 – 97.6) and the other with greater specificity 98.1% (95% CI 97.7 – 98.5).
DOI: 10.34740/kaggle/dsv/5632553
title: Egypt Gold Prices | Daily Updated
DOI: 10.5519/qd.lt4ngvdg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5536716 records)
DOI: 10.5281/zenodo.7907272
title: Cytotoxicity tests results of the aminooxazolo[5,4-d]pyrimidine derivatives

description: MTT results of the cytotoxicity tests in the 3T3 and RAW 264.7 cell lines of 7-aminooxazolo[5,4-d]pyrimidine derivatives obtained within the implementation of the MINIATURA-5 project of the Polish National Science Centre (NCN).
DOI: 10.5281/zenodo.7907271
title: Cytotoxicity tests results of the aminooxazolo[5,4-d]pyrimidine derivatives

description: MTT results of the cytotoxicity tests in the 3T3 and RAW 264.7 cell lines of 7-aminooxazolo[5,4-d]pyrimidine derivatives obtained within the implementation of the MINIATURA-5 project of the Polish National Science Centre (NCN).