cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15062611
title: Ticket to Ride Games

description: Description:

Ticket to Ride Games is a popular strategic board game where players compete to connect various cities on a map by placing their train pieces along specific routes. The objective is to complete the longest and most valuable routes while blocking opponents' paths. The game involves strategic planning, as each player's moves impact the overall board configuration and the availability of routes. This dataset is designed for enthusiasts, researchers, and Al developers interested in analyzing board game strategies, computer vision tasks, and data-driven game mechanics. It provides a comprehensive look at how different players approach the game, offering insights into decision-making processes, route optimization, and game dynamics.

Download Dataset

Content:

The dataset contains high-resolution images capturing various board configurations during the game, showcasing the players' city connections. The images are taken from four different angles to provide a complete view of the board's layout. Additionally, the dataset includes a well-structure CSV file that labels each player's city connections, detailing which cities have been successfully link by train routes. This labeling allows for in-depth analysis and pattern recognition, making it an ideal resource for those interest in game theory, Al training, or visual recognition models.

Key Features:



Images: Over [insert number] high-quality images of board configurations during gameplay, capturing different stages and strategies employe by players.

Angles: Each configuration is photograph from four distinct angles, ensuring comprehensive visual data for analysis.

CSV Labeling: The accompanying CSV file provides detail labeling of players' city connections, specifying which routes have been claim by each player. This structured data enables various analytical approaches, including statistical analysis, machine learning, and Al model training.

Versatile Applications: The dataset can be use for computer vision tasks, such as object detection and image segmentation, as well as for developing Al models to simulate or predict player strategies in board games.

Research Potential: Ideal for academic research, game development, and Al training, this dataset offers a rich source of data for exploring the complexities of board game strategies and player behaviors.


This dataset is sourced from Kaggle.

















01
DOI: 10.6084/m9.figshare.27129903
title: Structural recognition and stabilization of tyrosine hydroxylase by the J-domain protein DNAJC12

description: TH_DNAJC12-20240930.zip includes the cryo-EM maps and the PDB validation report for the TH:DNAJC12 complex

TH_DNAJC12(108-198)-20240930.zip includes the cryo-EM maps and the PDB validation report for the TH:DNAJC12(108-198) complex

TH(DA)_DNAJC12-20240930.zip includes the cryo-EM maps and PDB validation report for the TH(DA):DNAJC12 complex
DOI: 10.6084/m9.figshare.27129903.v1
title: Structural recognition and stabilization of tyrosine hydroxylase by the J-domain protein DNAJC12

description: TH_DNAJC12-20240930.zip includes the cryo-EM maps and the PDB validation report for the TH:DNAJC12 complex

TH_DNAJC12(108-198)-20240930.zip includes the cryo-EM maps and the PDB validation report for the TH:DNAJC12(108-198) complex

TH(DA)_DNAJC12-20240930.zip includes the cryo-EM maps and PDB validation report for the TH(DA):DNAJC12 complex
DOI: 10.15468/dl.cfekr6
title: Occurrence Download

description: A dataset containing 6057 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus rugosa Née"
 ]
}

 The dataset includes 6057 records from 190 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019983-250310093411724/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.27357093
title: Structurally heterogenous ribosomes cooperate in protein synthesis in single bacterial cells

description: This dataset supplements the manuscript on "Structurally heterogenous ribosomes cooperate in protein synthesis in single bacterial cells"
DOI: 10.6084/m9.figshare.27357093.v1
title: Structurally heterogenous ribosomes cooperate in protein synthesis in single bacterial cells

description: This dataset supplements the manuscript on "Structurally heterogenous ribosomes cooperate in protein synthesis in single bacterial cells"
DOI: 10.15468/dl.m42tur
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus runcinatifolia Trel. & C.H.Müll."
 ]
}

 The dataset includes 20 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019984-250310093411724/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.28304021.v1
title: A novel multi-scale microstructure to address the strength/ductility trade off in high strength steel for fusion reactors

description: Raw data presented on excel spreadsheet
DOI: 10.6084/m9.figshare.28236497
title: Injectable Bioadhesive and Lubricating Hydrogel with Polyphenol Mediated Single Atom Nanozyme for Rheumatoid Arthritis Therapy

description: The source data are provided with this paper
DOI: 10.6084/m9.figshare.28236497.v1
title: Injectable Bioadhesive and Lubricating Hydrogel with Polyphenol Mediated Single Atom Nanozyme for Rheumatoid Arthritis Therapy

description: The source data are provided with this paper
DOI: 10.6084/m9.figshare.24062091
title: Data and code

description: The raw data of CUE compiled from 68 references, its coordinates (latitude and longitude) and detrended CUE as well as the associated environmental variables. The code for detrend method and figures 1-4 in main text.
DOI: 10.6084/m9.figshare.24062091.v1
title: Data and code

description: The raw data of CUE compiled from 68 references, its coordinates (latitude and longitude) and detrended CUE as well as the associated environmental variables. The code for detrend method and figures 1-4 in main text.
DOI: 10.6084/m9.figshare.25866238
title: Data and R codes for Temporal stability of forest productivity declines over stand age at multiple spatial scales.

description: Our database contains species composition data and corresponding codes for calculating five stability components (species stability, species asynchrony, alpha stability, spatial asynchrony, and gamma stability) and three diversity components (functional alpha, beta, and gamma diversity) of 2886 plots between 1977 and 2022. Other factors include associated environmental variables such as temperature, water availability, and soil data, as well as functional trait data at species level. The meanings of variables in each data file and the functions of the code can be found in "README. txt"
DOI: 10.6084/m9.figshare.28106855
title: A LLM driven dataset on the spatiotemporal distributions of street and neighborhood crime in China

description: Crime is a significant social, economic, and legal issue. This paper presents an open-access spatiotemporal repository of street and neighborhood crime data, comprising approximately one million records of crimes in China, with specific geographic coordinates (latitude and longitude) and timestamps for each incident. The dataset is based on publicly available law court judgment documents. Artificial intelligence (AI) technologies are employed to extract crime events at the neighborhood or even building level from vast amounts of unstructured judicial text. This dataset enables more precise spatial analysis of crime incidents, offering valuable insights across interdisciplinary fields such as economics, sociology, and geography. It contributes significantly to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly in fostering sustainable cities and communities, and plays a crucial role in advancing efforts to reduce all forms of violence and related mortality rates.
DOI: 10.6084/m9.figshare.28092671
title: Hydro-geochemistry and Sr, C, S, and O isotopic compositions of major rivers on the Tibetan Plateau

description: Hydro-geochemistry and Sr, C, S, and O isotopic compositions of major rivers on the Tibetan Plateau, including the main channels and tributaries of Nu Jiang River (upper reaches of Salween River), Yarlung Tsangpo River (upper reaches of Brahmaputra River), the upper Yellow River, the upper Yangtze River and Lantsang Jiang River (upper reaches of Mekong River).
DOI: 10.48320/1f674834-db6b-475d-8f55-24cdb2174006
title: Readiness of the Ukrainian Population for Territorial Concessions (May 2022 – February 2025)
DOI: 10.5281/zenodo.15062639
title: Readiness of the Ukrainian Population for Territorial Concessions (May 2022 – February 2025) – Merged data from nationwide public opinion surveys conducted by KIIS from May 2022 to February 2025

description: The dataset includes data collected from a series of public opinion polls conducted by the Kyiv International Institute of Sociology (KIIS) from May 2022 to February 2025, on the question aimed at measuring the readiness of the Ukrainian population for territorial concessions to end the war. The question used was: 'Which of these statements about possible compromises to achieve peace with Russia do you agree with more?' The answer options were: 'To achieve peace as quickly as possible and maintain independence, Ukraine may give up some of its territories,' or 'Under no circumstances should Ukraine give up any of its territories, even if it means the war will last longer and there will be threats to maintaining independence.'
The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement).
The merged dataset includes data from 13 polls from May 2022 to February 2025 with a total of 18,215 respondents.
All survey waves were conducted with samples representative of the adult population (18 years and older) of Ukraine (within the territories controlled by the Ukrainian government as of February 24, 2022) using the CATI (computer-assisted telephone interview) method. The question was asked to either the full sample (2,000 respondents) or a subsample (1,000 respondents), depending on the survey wave.
The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook).
The Data Documentation (pdf file) also includes a short overview and discussion of survey results.

New in version 1.1. The previous version (v1.0) contained data from the beginning of the monitoring (May 2022) up to May 2024. This version (v1.1) includes data from three new survey waves, extending the coverage period to February 2025.
description: Published on Discuss Data, https://discuss-data.net/dataset/1f674834-db6b-475d-8f55-24cdb2174006/
Dataset creation: March 2025
description: Method(s) of data collection: Public Opinion PollMethod(s) of data analysis: Descriptive Statistics
DOI: 10.5281/zenodo.15062640
title: Readiness of the Ukrainian Population for Territorial Concessions (May 2022 – February 2025) – Merged data from nationwide public opinion surveys conducted by KIIS from May 2022 to February 2025

description: The dataset includes data collected from a series of public opinion polls conducted by the Kyiv International Institute of Sociology (KIIS) from May 2022 to February 2025, on the question aimed at measuring the readiness of the Ukrainian population for territorial concessions to end the war. The question used was: 'Which of these statements about possible compromises to achieve peace with Russia do you agree with more?' The answer options were: 'To achieve peace as quickly as possible and maintain independence, Ukraine may give up some of its territories,' or 'Under no circumstances should Ukraine give up any of its territories, even if it means the war will last longer and there will be threats to maintaining independence.'
The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement).
The merged dataset includes data from 13 polls from May 2022 to February 2025 with a total of 18,215 respondents.
All survey waves were conducted with samples representative of the adult population (18 years and older) of Ukraine (within the territories controlled by the Ukrainian government as of February 24, 2022) using the CATI (computer-assisted telephone interview) method. The question was asked to either the full sample (2,000 respondents) or a subsample (1,000 respondents), depending on the survey wave.
The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook).
The Data Documentation (pdf file) also includes a short overview and discussion of survey results.

New in version 1.1. The previous version (v1.0) contained data from the beginning of the monitoring (May 2022) up to May 2024. This version (v1.1) includes data from three new survey waves, extending the coverage period to February 2025.
description: Published on Discuss Data, https://discuss-data.net/dataset/1f674834-db6b-475d-8f55-24cdb2174006/
Dataset creation: March 2025
description: Method(s) of data collection: Public Opinion PollMethod(s) of data analysis: Descriptive Statistics
DOI: 10.15468/dl.xfwhj7
title: Occurrence Download

description: A dataset containing 6659 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amaranthus palmeri S.Watson"
 ]
}

 The dataset includes 6659 records from 284 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019985-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9jxf2w
title: Occurrence Download

description: A dataset containing 617 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus rysophylla Weath."
 ]
}

 The dataset includes 617 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019986-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bznfkt
title: Occurrence Download

description: A dataset containing 1175 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus sadleriana R.Br.ter"
 ]
}

 The dataset includes 1175 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019988-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48623/aperta.274391
title: Doğu Akdeniz'Deki Yeşil Deniz Kaplumbağası (Chelonia Mydas) Yuvalama Kumsallarındaki (Kazanlı, Akyatan Ve Samandağ) Yuva Mikrobiotası, Patojen Yoğunluğu Ve Yuvalardaki Oksijen Ve Sıcaklığın Yavru Başarısına Etkisi.

description: Nesli tehdit altında olan türlerin korunması için öncelikle türle ilgili elde edilebilecek olan tüm biyolojik ve ekolojik bilginin toplanması en önemli hususlardan bir tanesidir. Ektoterm canlılar olarak deniz kaplumbağalarının gelecekte iklim değişikliğinden etkilenmesi muhtemeldir ve dağılımlarını başlıca sıcaklık olmak üzere çeşitli parametreler etkilemektedir. Bu projenin çalışma konusu olan Ceholina mydas (Yeşil Kaplumbağa) dünya genelinde yayılış göstermekte ve Dünya genelinde dünya koruma birliğinin (IUCN) kırmızı listesinde tehlike altında (EN) kategorisinde yer almaktadır.

Bu bağlamda bu çalışma kapsamında varılmak istenen hedefler:Ülkemizdeki yeşil deniz kaplumbağası yuva mikrobiyotasını yeni nesil DNA dizileme yöntemiyle belirleyerek, denizden farklı uzaklıklarda örnekleme yapılan yuvalardaki mikrobiyal tür benzerlik ve farklılıklarını ortaya koymak. Denizden farklı uzaklıklarda örnekleme yapılan yuvalardaki yumurtaların STEF’ne neden olan fungal türleri izole edip tanılamak. Örnekleme yapılan kumsallardaki FSSC türlerinin filogenetik ilişkisini belirlemek. Üreme döneminde görülen STEF hastalığının o dönem için örnekleme yapılan kumsallardaki prevelans ve insidans farklılıklarını belirlemek.Yuvalarda kuluçka süresi boyunca ve kuluçka süresinin cinsiyeti belirleyen orta kuluçka döneminde yuva sıcaklıkları hakkında bilgi sahibi olmak ve kumsallar arası yuva sıcaklık farklarını ortaya koymak. Sıcaklık ve oksijen seviyesinin yavru çıkış başarısına ve yuva içindeki fungal gelişime etkisini belirlemek.

Kazanlı, Akyatan ve Samandağ kumsallarındann toplam 75 yuvanın NGS (Next-Generation Sequencing) metagenomik analizi ile mikrobiyota profilleri belirlenmiştir. Analiz için mikrobiyota cins seviyesindeki bolluk verileri kullanılmıştır. PCA (Principal Component Analysis) ve NMDS (Non-metric Multidimensional Scaling) analizleri kullanılarak topluluk yapılarının benzerliği araştırılmıştır

Yuvalama kumsalları arasında PCA analizine göre, birinci bileşen (PC1) varyansın %52.82’sini, ikinci bileşen (PC2) ise %17.98’ini açıklamaktadır (Şekil 29). Bu, mikrobiyota topluluklarının büyük bir kısmının PC1 boyunca farklılık gösterdiğini ancak genel olarak kumsalların tam olarak ayrılmadığını göstermektedir. NMDS analizi sonucunda da kumsallar arasındaki mikrobiyota dağılımı benzer bir şekilde dağılm göstermiştir (Şekil 29). Akyatan ve Samandağ örneklerinin belirli alanlarda yoğunlaşmasına rağmen, Kazanlı örneklerinin diğerleriyle daha farklı bölgede yoğunlaştığı görülmektedir. NMDS analizi, PCA ile benzer bir örüntü göstermiş, ancak örneklerin daha yayılmış olduğunu ortaya koymuştur. NMDS ve PCA birlikte değerlendirildiğinde, mikrobiyota topluluklarının tamamen farklı olmadığı, ancak belirli türlerin baskınlık gösterdiği düşünülebilir.
description: Dr. Öğr. Üyesi Sezgin KARAMAN'a katkılarından dolayı teşekkür ederiz.
DOI: 10.5285/47d0718d-7146-44d3-965c-60e62a48b8cc
title: Aerial survey of Gold Harbour, South Georgia, elephant seal colony during the 2023/24 season, flight DLPUS_214_32

description: Aerial survey of the Gold Harbour elephant seal colony, Mirounga leonina, during the 2023/24 season conducted using a remotely piloted aerial system (RPAS).

The aerial survey was conducted as part of the Darwin Plus (DPLUS) 214 project: Southwest Atlantic Elephant Seal Population Assessment (SAESPA).

Dataset includes the original images and log files, the post-processed imagery data, along with the resulting georeferenced DSM (digital surface model) and an orthorectified mosaic of the imagery.

This dataset is from flight number DPLUS_214_32 which was carried out on 2024-11-10.

This work was supported by the Overseas Territories Environment and Climate Fund under Grant Darwin Plus ref: DPLUS214: Southwest Atlantic Elephant Seal Population Assessment (SAESPA).
description: Data collection:
Aerial survey conducted using a RPAS (AgEagle eBee X). The survey consisted of collecting overlapping imagery of the target area, with a forward overlap of 95%. The target altitude during acquisition was 95 m AGL (above ground level).

Photogrammetry / final outputs:
Structure from motion photogrammetry was used to determine the exterior orientation (x, y, z, omega, phi and kappa (or roll, pitch, yaw)) of the camera during image acquisition using a combination of the onboard GNSS solution and common points within the images. Once the camera centre and orientation for each image was determined, a dense point cloud was extracted of the surface within the target area. The dense point cloud was then transformed into a DSM, which in turn was used to resample the images onto a map projection, removing camera perspective and terrain distortions, in a process called ortho-rectification and resulted in a single complete mosaic of the imagery.
description: Data collection equipment:

Unmanned aerial vehicle (UAV):
Manufacturer: AgEagle
Model: eBee X
Sensor: AgEagle SODA 3D
Flight management software: eMotion (version 3.23.0)

Post-flight processing software:


eMotion (version 3.23.0)
Photogrammetry software:
Pix4D (version 4.9.0.).
description: Coordinate reference system for outputs:
Projection: UTM Zone 23S
Horizontal datum: WGS84
Vertical datum: MSL (mean sea level), EGM2008 geoid

Resolution of final outputs:
DSM (2.1 cm)
Orthorectified mosaic (2.1 cm)

The camera is placed in the centre of the central component of the drone, with the LiDAR ground sensor immediately below it and offset from the camera. The GNSS antenna is at the front of the drone, offset in the opposite direction.

The sampling rate varied throughout the flight, depending on the speed of the platform.

Calibration was accounted for in processing.
DOI: 10.5285/9c4cb255-a754-4b6b-9748-d8b355e029de
title: Aerial survey of St Andrews Bay, South Georgia, elephant seal colony during the 2023/24 season, flight DLPUS_214_10

description: Aerial survey of St Andrews Bay elephant seal colony, Mirounga leonina, during the 2023/24 season conducted using a remotely piloted aerial system (RPAS).

The aerial survey was conducted as part of the Darwin Plus (DPLUS) 214 project: Southwest Atlantic Elephant Seal Population Assessment (SAESPA).

Dataset includes the original images and log files, the original RINEX GNSS (global navigation satellite system) base station files, the post-processed global navigation satellite system and imagery data, along with the resulting georeferenced DSM (digital surface model) and an orthorectified mosaic of the imagery.

This dataset is from flight number DPLUS_214_10 which was carried out on 2024-10-18.

This work was supported by the Overseas Territories Environment and Climate Fund under Grant Darwin Plus ref: DPLUS214: Southwest Atlantic Elephant Seal Population Assessment (SAESPA).
description: Data collection:
Aerial survey conducted using a RPAS (AgEagle eBee X). The survey consisted of collecting overlapping imagery of the target area, with a forward overlap of 80% and a side overlap of 60%. The target altitude during acquisition was 95 m AGL (above ground level).

Photogrammetry / final outputs:
Structure from motion photogrammetry was used to determine the exterior orientation (x, y, z, omega, phi and kappa (or roll, pitch, yaw)) of the camera during image acquisition using a combination of the onboard GNSS solution and common points within the images. Once the camera centre and orientation for each image was determined, a dense point cloud was extracted of the surface within the target area. The dense point cloud was then transformed into a DSM, which in turn was used to resample the images onto a map projection, removing camera perspective and terrain distortions, in a process called ortho-rectification and resulted in a single complete mosaic of the imagery.
description: Data collection equipment:

Unmanned aerial vehicle (UAV):
Manufacturer: AgEagle
Model: eBee X
Sensor: AgEagle SODA 3D
Flight management software: eMotion (version 3.23.0)

GNSS base station:
Manufacturer: Trimble
Receiver model: R9s
Antenna model: Zyphr Model 2

Post-flight processing software:

Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP, Version 3)
eMotion (version 3.23.0)
Photogrammetry software:
Pix4D (version 4.9.0.).
description: Coordinate reference system for outputs:
Projection: UTM Zone 23S
Horizontal datum: WGS84
Vertical datum: MSL (mean sea level), EGM2008 geoid

Resolution of final outputs:
DSM (2.1 cm)
Orthorectified mosaic (2.1 cm)

The camera is placed in the centre of the central component of the drone, with the LiDAR ground sensor immediately below it and offset from the camera. The GNSS antenna is at the front of the drone, offset in the opposite direction.

The sampling rate varied throughout the flight, depending on the speed of the platform.

Calibration was accounted for in processing.