cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4v59tr
title: Occurrence Download

description: A dataset containing 116529 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany"
 ]
}

 The dataset includes 116529 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050277-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sue8dw
title: Occurrence Download

description: A dataset containing 188 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Hoffmannia pittieri"
 ]
}

 The dataset includes 188 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050308-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ajdxun
title: Occurrence Download

description: A dataset containing 33543 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rubus phoenicolasius",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 33543 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050303-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rb7mpm
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meetnetten.be - Area count for Eresus sandaliatus in Flanders, Belgium"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050305-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpwgsu
title: Occurrence Download

description: A dataset containing 11967 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is MUNTJAC_ANTWERP - Camera trap observations of Chinese muntjac in forested areas near Antwerp (Belgium)"
 ]
}

 The dataset includes 11967 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050304-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25810/qt7k-6r09.76
title: Swedish Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - August 2025

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.15468/dl.rvfzqk
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meetnetten.be - Bat maternity roosts counts Flanders, Belgium"
 ]
}

 The dataset includes 399 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050310-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uxr7hu
title: Occurrence Download

description: A dataset containing 656 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Hoffmannia psychotriifolia"
 ]
}

 The dataset includes 656 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050312-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b49jfb
title: Occurrence Download

description: A dataset containing 9305 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Meetnetten.be - Bat winter roosts counts (totals per room/section) Flanders, Belgium"
 ]
}

 The dataset includes 9305 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050314-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30096589
title: S1 Dataset. Succulent Light and Temperature Data.xlsx

description: Effects of day/night air temperature set points (MDT) of 34/28 (31), 31/25 (28), 28/22 (25), 25/19 (22), or 22/16 (19°C) °C (12-h/12-h day/night) and three DLIs using no shade cloth or 30% or 50 % shade cloth on growth and development of greenhouse grown tree houseleek (<i>Aeonium</i> <i>arboreum</i>) ‘Nigrum’ (Rep. 1 only), round-leafed navel-wort (<i>Cotyledon</i> <i>orbiculata</i>) ‘Silver Peak’, Mexican hens and chicks (<i>Echeveria hybrid</i>) ‘Pollux’, kalanchoe (<i>Kalanchoe villiosa</i>), panda plant (<i>K.</i> <i>tomentosa</i>), coral senecio (<i>Senecio fulgens</i>) ‘Amke’ (Rep. 1 only), and senecio (<i>Senecio ficoides</i>) ‘Mount Everest’ (Rep. 2 only).
DOI: 10.6084/m9.figshare.30096589.v1
title: S1 Dataset. Succulent Light and Temperature Data.xlsx

description: Effects of day/night air temperature set points (MDT) of 34/28 (31), 31/25 (28), 28/22 (25), 25/19 (22), or 22/16 (19°C) °C (12-h/12-h day/night) and three DLIs using no shade cloth or 30% or 50 % shade cloth on growth and development of greenhouse grown tree houseleek (<i>Aeonium</i> <i>arboreum</i>) ‘Nigrum’ (Rep. 1 only), round-leafed navel-wort (<i>Cotyledon</i> <i>orbiculata</i>) ‘Silver Peak’, Mexican hens and chicks (<i>Echeveria hybrid</i>) ‘Pollux’, kalanchoe (<i>Kalanchoe villiosa</i>), panda plant (<i>K.</i> <i>tomentosa</i>), coral senecio (<i>Senecio fulgens</i>) ‘Amke’ (Rep. 1 only), and senecio (<i>Senecio ficoides</i>) ‘Mount Everest’ (Rep. 2 only).
DOI: 10.17605/osf.io/7my89
title: Doctorates- TTD-Debt-GRE
DOI: 10.17632/bm4v4rzdyd
title: Detection of Right Ventricular Dysfunction Based on Pulmonary Embolism Dataset

description: This study included patients diagnosed with acute pulmonary embolism (PE) based on contrast-enhanced multidetector computed tomography (CT). Eligibility required the availability of echocardiography and lower-extremity venous Doppler ultrasound reports. Exclusion criteria were: pregnancy, absence of echocardiographic or Doppler studies, history of chronic thromboembolic pulmonary hypertension, known right heart dysfunction prior to PE, and age under 18 years. There are no individual characteristics (name-surname, ID number, etc.) that define the patients in our dataset. All individual data are anonymized.
Each row in the dataset provides information about a patient's characteristics. The first column shows the gender of the PE patients, the second column shows their ages, and the other columns show various characteristics used in the diagnosis of RVD. The last column shows whether the PE patients were diagnosed with RVD by the physician. This dataset contains a total of 163 cases. There are a total of 163 cases in this dataset. 62 cases are RVD-positive and 101 cases are RVD-negative.This dataset can be used in the artificial intelligence models.

Table 1. List of features indicating the range of changes in values.
№ Feature name Value range Value description
1 Sex ( 0-1 ) (1:Male, 0:Female)
2 Age ( 22-93 ) -
3 Thrombus Main Pulmonary ( 0-1 ) (1: Presence, 0: Absence)
4 Thrombus Lobe Arteries ( 0-1 ) (1: Presence, 0: Absence)
5 Thrombus Segment ( 0-1 ) (1: Presence, 0: Absence)
6 Thrombus Subsegment ( 0-1 ) (1: Presence, 0: Absence)
7 Thrombus ( 0-1 ) (1: Unilateral, 0: Bilateral)
8 DVT ( 0-1 ) (1: Presence, 0: Absence)
9 DVT Distal Ven ( 0-1 ) (1: Presence, 0: Absence)
10 DVT Proksimal Ven ( 0-1 ) (1: Presence, 0: Absence)
11 Appearance of DVT ( 0-2 ) (Absence: 0 Unilateral :1 Bilateral: 
12 Comorbid Disease ( 0-1 ) (1: Presence, 0: Absence)
13 Malignancy ( 0-1 ) (1: Presence, 0: Absence)
14 Diabetes Mellitus ( 0-1 ) (1: Presence, 0: Absence)
15 Hypertension ( 0-1 ) (1: Presence, 0: Absence)
16 Coronary Artery Disease (CAD) ( 0-1 ) (1: Presence, 0: Absence)
17 COPD ( 0-1 ) (1: Presence, 0: Absence)
18 Asthma ( 0-1 ) (1: Presence, 0: Absence)
19 Cerebrovascular Occlusion ( 0-1 ) (1: Presence, 0: Absence)
20 Heart Failure ( 0-1 ) (1: Presence, 0: Absence)
*21 Right Ventricular Involvement ( 0-1 ) (1: Presence, 0: Absence)

Note: *Target variable
DVT: Deep vein thrombosis.

Please cite the following four articles when you use any of these datasets:
1) Huyut, M.T., Velichko, A., Belyaev, M. et al. Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms. Sci Rep 15, 25262 (2025). https://doi.org/10.1038/s41598-025-00274-1. 
2) Huyut, M.T. et. al., "Detection of Right Ventricular Dysfunction Using LogNNet Neural Network Model Based on Pulmonary Embolism Data Set," Eastern Journal of Medicine , vol.29, no.1, pp.118-128, 2024.
DOI: 10.17632/bm4v4rzdyd.2
title: Detection of Right Ventricular Dysfunction Based on Pulmonary Embolism Dataset

description: This study included patients diagnosed with acute pulmonary embolism (PE) based on contrast-enhanced multidetector computed tomography (CT). Eligibility required the availability of echocardiography and lower-extremity venous Doppler ultrasound reports. Exclusion criteria were: pregnancy, absence of echocardiographic or Doppler studies, history of chronic thromboembolic pulmonary hypertension, known right heart dysfunction prior to PE, and age under 18 years. There are no individual characteristics (name-surname, ID number, etc.) that define the patients in our dataset. All individual data are anonymized.
Each row in the dataset provides information about a patient's characteristics. The first column shows the gender of the PE patients, the second column shows their ages, and the other columns show various characteristics used in the diagnosis of RVD. The last column shows whether the PE patients were diagnosed with RVD by the physician. This dataset contains a total of 163 cases. There are a total of 163 cases in this dataset. 62 cases are RVD-positive and 101 cases are RVD-negative.This dataset can be used in the artificial intelligence models.

Table 1. List of features indicating the range of changes in values.
№ Feature name Value range Value description
1 Sex ( 0-1 ) (1:Male, 0:Female)
2 Age ( 22-93 ) -
3 Thrombus Main Pulmonary ( 0-1 ) (1: Presence, 0: Absence)
4 Thrombus Lobe Arteries ( 0-1 ) (1: Presence, 0: Absence)
5 Thrombus Segment ( 0-1 ) (1: Presence, 0: Absence)
6 Thrombus Subsegment ( 0-1 ) (1: Presence, 0: Absence)
7 Thrombus ( 0-1 ) (1: Unilateral, 0: Bilateral)
8 DVT ( 0-1 ) (1: Presence, 0: Absence)
9 DVT Distal Ven ( 0-1 ) (1: Presence, 0: Absence)
10 DVT Proksimal Ven ( 0-1 ) (1: Presence, 0: Absence)
11 Appearance of DVT ( 0-2 ) (Absence: 0 Unilateral :1 Bilateral: 
12 Comorbid Disease ( 0-1 ) (1: Presence, 0: Absence)
13 Malignancy ( 0-1 ) (1: Presence, 0: Absence)
14 Diabetes Mellitus ( 0-1 ) (1: Presence, 0: Absence)
15 Hypertension ( 0-1 ) (1: Presence, 0: Absence)
16 Coronary Artery Disease (CAD) ( 0-1 ) (1: Presence, 0: Absence)
17 COPD ( 0-1 ) (1: Presence, 0: Absence)
18 Asthma ( 0-1 ) (1: Presence, 0: Absence)
19 Cerebrovascular Occlusion ( 0-1 ) (1: Presence, 0: Absence)
20 Heart Failure ( 0-1 ) (1: Presence, 0: Absence)
*21 Right Ventricular Involvement ( 0-1 ) (1: Presence, 0: Absence)

Note: *Target variable
DVT: Deep vein thrombosis.

Please cite the following four articles when you use any of these datasets:
1) Huyut, M.T., Velichko, A., Belyaev, M. et al. Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms. Sci Rep 15, 25262 (2025). https://doi.org/10.1038/s41598-025-00274-1. 
2) Huyut, M.T. et. al., "Detection of Right Ventricular Dysfunction Using LogNNet Neural Network Model Based on Pulmonary Embolism Data Set," Eastern Journal of Medicine , vol.29, no.1, pp.118-128, 2024.
DOI: 10.17632/6dmdfz3nhd
title: velocity correction factor

description: Variations in the velocity correction factor under different conditions of slope gradient, flow discharge, sediment load, and soil type
DOI: 10.17632/6dmdfz3nhd.1
title: velocity correction factor

description: Variations in the velocity correction factor under different conditions of slope gradient, flow discharge, sediment load, and soil type
DOI: 10.17632/x8k3nj2h8j
title: New Efficient D‐π‐A and A‐π‐A Structured Type I Radical Photoinitiators for Additive Manufacturing Nanomaterials Preparation

description: The dataset comprises raw experimental results obtained from several complementary characterization techniques and processes. Specifically, it includes UV-Vis spectral measurements, photopolymerization studies of different material types, photorheological analyses of the prepared compositions, as well as data collected during 3D printing experiments. These raw results provide the basis for further processing, analysis, and interpretation, and are intended to ensure transparency, reproducibility, and detailed documentation of the conducted research.
DOI: 10.17632/x8k3nj2h8j.1
title: New Efficient D‐π‐A and A‐π‐A Structured Type I Radical Photoinitiators for Additive Manufacturing Nanomaterials Preparation

description: The dataset comprises raw experimental results obtained from several complementary characterization techniques and processes. Specifically, it includes UV-Vis spectral measurements, photopolymerization studies of different material types, photorheological analyses of the prepared compositions, as well as data collected during 3D printing experiments. These raw results provide the basis for further processing, analysis, and interpretation, and are intended to ensure transparency, reproducibility, and detailed documentation of the conducted research.
DOI: 10.7910/dvn/3laovi
title: Replication Data for: Appendix A. Codebook for GCC AI Strategy Document Analysis (v2)

description: This codebook provides a structured framework for analyzing the six Gulf Cooperation Council (GCC) National AI Strategies (NASs) with a focus on capacity-building references. It links policy priorities explicitly to the constructs used in our empirical model, Technical Infrastructure (TI), Organizational Capability (OR), and Governance Environment (GE), and supports alignment with survey items Q2–Q12. The codebook combines qualitative categorization and quantitative weighting. Each NAS passage is coded into a primary category (TI, OR, or GE) using well-defined sub-codes, while secondary tags capture intended outcomes (AIO: service efficiency, citizen trust, performance) and contextual dimensions (for example, sector, localization, cultural factors). This ensures that both policy intentions and implementation pathways are captured consistently across countries. To assess emphasis on specific AI capabilities (for example, NLP, robotics, cybersecurity), the codebook applies term-salience analysis (TF–IDF) within the coded passages. This allows us to differentiate between universally mentioned enablers, such as AI ethics or cybersecurity, and more specialized but heavily emphasized capabilities, such as deep learning or robotics, that feature in only some national strategies. The design emphasizes reproducibility and rigor: coders follow clear inclusion/exclusion rules, use EN/AR synonym lexicons, and apply double-coding with inter-coder reliability checks (target κ ≥ 0.79). Outputs include normalized construct-level signals per country and comparative TF–IDF tables, which are then triangulated with survey and PLS-SEM findings. In sum, the codebook acts as a bridge between policy texts and empirical analysis, enabling transparent, comparable, and replicable measurement of AI adoption enablers across the GCC.
DOI: 10.17632/w3x8p3bf42.1
title: Actinic Keratosis in dermatoscopy and High-Frequency Ultrasound

description: This dataset consist of 200 HFUS and dermatoscopic images registered from 63 patients with actinic keratosis (AK). All experiments were performed following the protocol approved by the Bioethics Committee at the Poznan University of Medical Sciences, under reference number 523/23 on 29 June 2023. This study was conducted at the Department of Dermatology of Poznan University of Medical Sciences from June 2023 to April 2024. For each marker (AK site) dermatoscopy picture were taken (iPhone 8 camera coupled to DermLite DL5, 10× magnification) and then high-frequency images (DermaScan C, a Cortex Technology, Aalborg, Denmark device, linear 20 MHz probe) were taken as well. 

For each lesion, the clinico-dermatoscopic classification of each AK based on a three-point Zalaudek scale was performed by 2 experienced dermatologists. In this dataset, 115 markers were classified as AK1, 51 as AK2 and 34 as AK3. Each patient is codes as PXXX, and each corresponding markers for patient are coded as PXXX_mX. Dermatosopy and HFUS images have corresponding names in folders HFUS and Dermatoscopic. Patient info (age and sex are presented in file patientID_age_sex.csv, where 0:Female, 1:Male). File markersID_staging.csv contains stage corresponding to each markers (where 1:AK1, 2:AK2, 3:AK3).

This research was supported by National Science Centre, Poland, research project No. 2023/07/X/ST6/00861 “The use of artificial intelligence methods in the diagnosis of actinic keratosis based on multimodal image data” and No. 2023/07/X/NZ5/00867 “The importance of high-frequency ultrasound in the assessment of actinic keratosis and the cancerization field”.
DOI: 10.17632/hkc574byp9
title: Lead Partitioning on Birnessite during Pre-Phase Transition

description: Data of "Lead Partitioning on Birnessite during Pre-Phase Transition"
DOI: 10.17632/hkc574byp9.1
title: Lead Partitioning on Birnessite during Pre-Phase Transition

description: Data of "Lead Partitioning on Birnessite during Pre-Phase Transition"
DOI: 10.17632/x5ktg398s9
title: Dataset of A Framework for Developing External Key Performance Indicators Using Google Trends and Multi-Criteria Decision-Making

description: Datasets Description
To support the development and validation of the EKPI framework, three datasets were collected directly from Google platforms, covering a ten-year period (2014–2024). These datasets were structured in CSV format and include:
1. Google Results Dataset
This dataset captures the number of Google-indexed pages that mention each target brand over a ten-year period (2014–2024). To extract this data, we employed a monthly querying strategy using Google Search’s built-in time filter capabilities. Specifically, the following advanced search operator was used for each brand and month:
"keyword" after:YYYY-MM-DD before:YYYY-MM-DD
For example, the query:
"Brand Name" after:2014-01-01 before:2014-02-01
retrieves the number of indexed pages containing the keyword within January 2014. This approach was applied for each month over the study period and for both brands (“Hamrah-e-Aval” and “Irancell”), resulting in a longitudinal dataset representing the digital content footprint of each brand across time.
All collected monthly results were stored in tabular format, forming the basis for indicators related to Share of Voice(SOV) in Google.

2. Google Trends Timeline Dataset
The second dataset was directly downloaded from the Google Trends platform using its official export feature. The "Interest Over Time" graph in Google Trends provides a normalized time series of search volume for a given keyword or brand. For each brand, data was exported as a CSV file by selecting the appropriate time range (2014–2024) and region (Iran). This resulted in a continuous timeline of search popularity, used to calculate indicators such as:
- Brand Awareness in Google (BAG)
- Growth Rate of Awareness in Google (BAGR)
The exported file includes timestamps and corresponding interest scores (0–100) normalized by Google.

3. Google Trends Related Queries Dataset
The third dataset consists of monthly related search queries obtained from the “Related Queries” section of Google Trends for both target brands. These related queries offer insights into specific user intents and behaviors beyond the main brand search.
For each month between 2014 and 2024, related query data was downloaded as CSV files using Google Trends' built-in export functionality. To enrich this dataset, we manually added a sentiment column to each query. Specifically:
- A subset of 3,762 unique queries was labeled by three independent annotators as positive, negative, or neutral based on domain-specific criteria.
- A domain expert finalized the annotation where disagreements occurred.
- These labeled queries were later used to train and validate a hybrid sentiment analysis model combining lexicon-based[36] and deep learning techniques.
 -All 47,000 queries extracted over 10 years were automatically labeled by the trained model.

4. lexicon-dictionary