cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15364297
title: MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

description: Datasets provided in this storage are introduced in the paper: MiNT: Multi-Network Training forTransfer Learning on Temporal Graphs



Each .csv file represents all transactions of the token network that has the same name as the file name (<tokenname.csv>)

Each transaction corresponds to a row in each file.

Each transaction has:



 blockNumber :  is the block ID of Ethereum that includes this transaction

timestamp: time that the transaction is made in UNIX timestamp format

tokenAddress : the address that specifies a unique ERC20 token

from: address of sender

to: address of receiver

value: the amount the transaction

fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks 



To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network.



Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to

Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)

We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py



However, we also provide raw .csv  to divide into generate edgeslist and label with a different setting.

There are two set of labels for two different tasks, MiNT_labels_Edge_GS is for Edge Growth or Shrink and MiNT_labels_LargeConComp_GS is for Largest Connect Component Growth or Shrink task


 
description: Temporal Graph Learning (TGL) aims to discover patterns on evolving networks or temporal graphs, and leverage such patterns to predict future interactions. However, most existing work focuses on learning from single networks in isolation, leaving the question of cross-network generalization largely unexplored. In this study, we introduce a new benchmark of 84 real-world temporal transaction networks and propose Temporal Multi-network Training (MiNT), a pre-training framework designed to capture transferable temporal dynamics across diverse networks. We train MiNT models on up to 64 transaction networks and evaluate their generalization ability on 20 held-out, unseen networks. Our results show that MiNT consistently outperforms individually trained models, revealing a strong relation between the number of pre-training networks and transfer performance. These findings highlight scaling trends in temporal graph learning and underscore the importance of network diversity in improving generalization. This work establishes the first large-scale benchmark for studying transferability in TGL and lays the groundwork for developing Temporal Graph Foundation Models. Our code is available at https://github.com/benjaminnNgo/ScalingTGNs
DOI: 10.21233/xxbr-dz62
title: Little Ball Club Lake water chemistry dataset

description: Raw data for the Little Ball Club Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/0yb2-3w38
title: Clark State Fishing Lake water chemistry dataset

description: Raw data for the Clark State Fishing Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.2dad5a
title: Occurrence Download

description: A dataset containing 1053 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carpinus tschonoskii Maxim.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1053 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024097-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/cw5n-bc98
title: Fallison Bog (Trout D) testate amoebae surface sample dataset

description: Raw data for the Fallison Bog (Trout D) testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/38j5-ke14
title: AFG-012 [Afo-110] vertebrate fauna dataset

description: Raw data for the AFG-012 [Afo-110] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/m0j7-mr04
title: Velperbroek circuit pollen dataset

description: Raw data for the Velperbroek circuit pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/0npq-1j39
title: Okawame Sunagomata ostracode surface sample dataset

description: Raw data for the Okawame Sunagomata ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.h9myfp
title: Occurrence Download

description: A dataset containing 4265968 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 4265968 records from 7943 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024059-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3twuqn
title: Occurrence Download

description: A dataset containing 66458 species occurrences available in GBIF matching the query:
{
 "and" : [
 "KingdomKey is Fungi",
 "GadmGid is RUS.30_1",
 {
 "not" : {
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
 }
 },
 {
 "not" : {
 "Issue" : [
 "is one of (Country coordinate mismatch, Continent coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 66458 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077273-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y2xzw7
title: Occurrence Download

description: A dataset containing 1488163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lathyrus L.",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1488163 records from 3833 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077305-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11837464
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17632/xs39gcwsdc
title: Evaluation of Machine Learning Models and SHAP Visualization on the Diagnosis of Triple Negative and Non-Triple Negative Breast Cancer

description: Data and codes for triple negative breast cancer and non triple negative breast cancer.
DOI: 10.17632/xs39gcwsdc.1
title: Evaluation of Machine Learning Models and SHAP Visualization on the Diagnosis of Triple Negative and Non-Triple Negative Breast Cancer

description: Data and codes for triple negative breast cancer and non triple negative breast cancer.
DOI: 10.17632/sdk98y8cjn.1
title: Shoulder ROM Data of Male Assembly Workers with Shoulder Impingement Syndrome

description: This dataset contains shoulder range of motion (ROM) measurements collected from male automobile assembly line workers as part of a study investigating shoulder impingement syndrome. The data were originally gathered through a clinical assessment and have since been anonymized for secondary analysis and public sharing. Each row represents a single individual.
DOI: 10.17632/sdk98y8cjn
title: Shoulder ROM Data of Male Assembly Workers with Shoulder Impingement Syndrome

description: This dataset contains shoulder range of motion (ROM) measurements collected from male automobile assembly line workers as part of a study investigating shoulder impingement syndrome. The data were originally gathered through a clinical assessment and have since been anonymized for secondary analysis and public sharing. Each row represents a single individual.
DOI: 10.17632/sdk98y8cjn.2
title: Shoulder ROM Data of Male Assembly Workers with Shoulder Impingement Syndrome

description: This dataset contains shoulder range of motion (ROM) measurements collected from male automobile assembly line workers as part of a study investigating shoulder impingement syndrome. The data were originally gathered through a clinical assessment and have since been anonymized for secondary analysis and public sharing. Each row represents a single individual.
DOI: 10.17632/wgcnp6zmtf
title: Buhari dogan innovation

description: Demystifying the adoption process eco-innovation, green financial policy, environmental tax: A Step Toward Carbon Neutrality in OECD Countries
DOI: 10.17632/wgcnp6zmtf.1
title: Buhari dogan innovation

description: Demystifying the adoption process eco-innovation, green financial policy, environmental tax: A Step Toward Carbon Neutrality in OECD Countries
DOI: 10.17632/k3xs4vx37m.1
title: Trust, Security, and Digital Finance as Catalysts for Business Success in Bandung

description: This research focuses on the following constructs:

1. Security in Digital Payments

2. Trust in Digital Payments

3. FinTech Service Adoption

4. Business Performance

The purpose of this research is to investigate how the perceived security and trust in digital payments influence the adoption of FinTech services among Micro, Small, and Medium Enterprises (MSMEs) in Bandung, Indonesia. Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, this study examines how these perceptions ultimately impact MSME business performance through improved operations, customer engagement, and learning processes. The theoretical framework incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT), Resource-Based View (RBV), and Balanced Scorecard (BSC) perspectives.

Data were collected through structured questionnaires from 400 MSME respondents in Bandung, with validation conducted through SmartPLS 4.
DOI: 10.17632/k3xs4vx37m.2
title: Trust, Security, and Digital Finance as Catalysts for Business Success in Bandung

description: This research focuses on the following constructs:

1. Security in Digital Payments

2. Trust in Digital Payments

3. FinTech Service Adoption

4. Business Performance

The purpose of this research is to investigate how the perceived security and trust in digital payments influence the adoption of FinTech services among Micro, Small, and Medium Enterprises (MSMEs) in Bandung, Indonesia. Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, this study examines how these perceptions ultimately impact MSME business performance through improved operations, customer engagement, and learning processes. The theoretical framework incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT), Resource-Based View (RBV), and Balanced Scorecard (BSC) perspectives.

Data were collected through structured questionnaires from 400 MSME respondents in Bandung, with validation conducted through SmartPLS 4.
DOI: 10.17632/k3xs4vx37m.3
title: Trust and Security in Digital Payments Drive Small Business Growth in Bandung

description: This research focuses on the following constructs:

1. Security in Digital Payments

2. Trust in Digital Payments

3. FinTech Service Adoption

4. Business Performance

The purpose of this research is to investigate how the perceived security and trust in digital payments influence the adoption of FinTech services among Micro, Small, and Medium Enterprises (MSMEs) in Bandung, Indonesia. Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, this study examines how these perceptions ultimately impact MSME business performance through improved operations, customer engagement, and learning processes. The theoretical framework incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT), Resource-Based View (RBV), and Balanced Scorecard (BSC) perspectives.

Data were collected through structured questionnaires from 400 MSME respondents in Bandung, with validation conducted through SmartPLS 4.
DOI: 10.15468/dl.vdwhbm
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carabus parreyssi Palliardi, 1825"
 ]
}

 The dataset includes 36 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001832-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5mvrx4v62z.1
title: Student Insomnia and Educational Outcomes Dataset

description: This dataset consists of 791 rows (responses) and 16 columns (features), focusing on the relationship between insomnia and its impact on educational outcomes. It includes self-reported data on sleep patterns, quality, fatigue, stress levels, academic performance, and lifestyle habits. The survey was conducted using Google Forms, ensuring broad accessibility and ease of participation.

Data Collection: The data was collected through an online survey administered via Google Forms in Oct-Nov 2024. Respondents were asked to provide insights into their sleep behaviors and the effects on their academic and daily activities.

Key Features:
1. Demographics: Year of study and gender.
2. Sleep Patterns: Frequency of difficulty falling asleep, hours of sleep, night awakenings, and overall sleep quality.
3. Cognitive and Academic Effects: Impact on concentration, fatigue, class attendance, assignment completion, and overall academic performance.
4. Lifestyle Factors: Electronic device usage before sleep, caffeine consumption, and physical activity frequency.
5. Stress Levels: Self-reported stress related to academic workload.

This dataset can be used for:
1. Machine learning analysis to model and predict academic performance based on sleep and lifestyle factors.
2. Statistical studies investigating the connection between sleep disturbances and educational outcomes.
3. Developing behavioral and educational interventions to improve student well-being and performance.

Format: The dataset consists of 16 columns in categorical or ordinal formats. It contains 791 rows with no missing data, making it ready for analytics and machine learning applications.
DOI: 10.17632/5mvrx4v62z.2
title: Student Insomnia and Educational Outcomes Dataset

description: This dataset consists of 985 rows (responses) and 16 columns (features), focusing on the relationship between insomnia and its impact on educational outcomes. It includes self-reported data on sleep patterns, quality, fatigue, stress levels, academic performance, and lifestyle habits. The survey was conducted using Google Forms, ensuring broad accessibility and ease of participation.

Data Collection: The data was collected through an online survey administered via Google Forms in Oct-Nov 2024. Respondents were asked to provide insights into their sleep behaviors and the effects on their academic and daily activities.

Key Features:
1. Demographics: Year of study and gender.
2. Sleep Patterns: Frequency of difficulty falling asleep, hours of sleep, night awakenings, and overall sleep quality.
3. Cognitive and Academic Effects: Impact on concentration, fatigue, class attendance, assignment completion, and overall academic performance.
4. Lifestyle Factors: Electronic device usage before sleep, caffeine consumption, and physical activity frequency.
5. Stress Levels: Self-reported stress related to academic workload.

This dataset can be used for:
1. Machine learning analysis to model and predict academic performance based on sleep and lifestyle factors.
2. Statistical studies investigating the connection between sleep disturbances and educational outcomes.
3. Developing behavioral and educational interventions to improve student well-being and performance.

Format: The dataset consists of 16 columns in categorical or ordinal formats. It contains 785 rows with no missing data, making it ready for analytics and machine learning applications.
DOI: 10.25810/00zk-yj28.76
title: Norwegian Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - July 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: 10.3535/8mk-bb8-7s6
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wr3-npa-pel
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jn9-73q-y2g
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/npz-3cz-7pf
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ltt-ahs-qfl
title: †Nummulites planulatus (Lamarck, 1804)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/235-xsj-b3e
title: †Nummulites Lamarck, 1801

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/m3j-wyc-1c6
title: †Favorites Tschigova, 1977

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4mn-vxe-h5r
title: Cnidaria

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fcj-p7j-1vh
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/amr-hzk-ck1
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/dbl-tbf-ptf
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7rj-qz6-lt1
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3yt-5x7-xwe
title: Balclutha Kirkaldy, 1900

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fkh-qdf-l2y
title: Bos taurus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5dk-j8l-knh
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pbq-502-8s5
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.