cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.31391
title: Sentiment classification of financial social media text
title: Sentiment classification of financial social media text

description: It is  a financial social media text sentiment classification dataset. Given financial social media text, the model needs to classify the sentiment of the text into negative-neutral positive categories, with evaluation measured by accuracy. It is sourced from posts by stockhold ers and bloggers on the Stockbar and Xueqiu websites, categorized as “积极 (positive)”, “中性 (neutral)”, and “消极 (negative)”. 
description: It is  a financial social media text sentiment classification dataset. Given financial social media text, the model needs to classify the sentiment of the text into negative-neutral positive categories, with evaluation measured by accuracy. It is sourced from posts by stockhold ers and bloggers on the Stockbar and Xueqiu websites, categorized as “积极 (positive)”, “中性 (neutral)”, and “消极 (negative)”.
DOI: 10.6084/m9.figshare.30692057
title: <b>Spatial landscapes of immune checkpoint inhibitor-induced colitis</b>

description: Immune-related adverse events (irAEs) compromise the effective use of immune checkpoint inhibitors (ICIs). Although CD8+ T cell overactivation is central to anti-PD-1-related irAEs, the local immunological mechanisms contributing to colitic irAEs remain poorly understood. We collected colonic tissue samples from patients with and without colitic irAE following anti-PD-1 treatment. Integrating spatial transcriptomics with comprehensive computational analysis, we examined the local transcriptional profiles enriched with CD8+ T cell infiltration in colitic irAE. Integrated computational analysis identified ten key candidate mediators of colitic irAE, which validated in independent single-cell RNA sequencing datasets and tissue sections for representative candidates CLDN3 and TFF3. Epithelial barrier disruption and epithelial-mesenchymal transition contributed to colitic irAE pathogenesis, together with an increase in regulatory T cells, endothelial cells, fibroblasts, and macrophages. Expression of key colitic irAE regulator genes was associated with severity of adverse events and endoscopic features. Our findings indicate epithelial barrier disruption and a distinct immunological landscape in colitic irAE to inform diagnostic and therapeutic strategies.
DOI: 10.6084/m9.figshare.30692057.v2
title: <b>Spatial landscapes of immune checkpoint inhibitor-induced colitis</b>

description: Immune-related adverse events (irAEs) compromise the effective use of immune checkpoint inhibitors (ICIs). Although CD8+ T cell overactivation is central to anti-PD-1-related irAEs, the local immunological mechanisms contributing to colitic irAEs remain poorly understood. We collected colonic tissue samples from patients with and without colitic irAE following anti-PD-1 treatment. Integrating spatial transcriptomics with comprehensive computational analysis, we examined the local transcriptional profiles enriched with CD8+ T cell infiltration in colitic irAE. Integrated computational analysis identified ten key candidate mediators of colitic irAE, which validated in independent single-cell RNA sequencing datasets and tissue sections for representative candidates CLDN3 and TFF3. Epithelial barrier disruption and epithelial-mesenchymal transition contributed to colitic irAE pathogenesis, together with an increase in regulatory T cells, endothelial cells, fibroblasts, and macrophages. Expression of key colitic irAE regulator genes was associated with severity of adverse events and endoscopic features. Our findings indicate epithelial barrier disruption and a distinct immunological landscape in colitic irAE to inform diagnostic and therapeutic strategies.
DOI: 10.6084/m9.figshare.30692057.v1
title: <b>Spatial landscapes of immune checkpoint inhibitor-induced colitis</b>

description: Immune-related adverse events (irAEs) compromise the effective use of immune checkpoint inhibitors (ICIs). Although CD8+ T cell overactivation is central to anti-PD-1-related irAEs, the local immunological mechanisms contributing to colitic irAEs remain poorly understood. We collected colonic tissue samples from patients with and without colitic irAE following anti-PD-1 treatment. Integrating spatial transcriptomics with comprehensive computational analysis, we examined the local transcriptional profiles enriched with CD8+ T cell infiltration in colitic irAE. Integrated computational analysis identified ten key candidate mediators of colitic irAE, which validated in independent single-cell RNA sequencing datasets and tissue sections for representative candidates CLDN3 and TFF3. Epithelial barrier disruption and epithelial-mesenchymal transition contributed to colitic irAE pathogenesis, together with an increase in regulatory T cells, endothelial cells, fibroblasts, and macrophages. Expression of key colitic irAE regulator genes was associated with severity of adverse events and endoscopic features. Our findings indicate epithelial barrier disruption and a distinct immunological landscape in colitic irAE to inform diagnostic and therapeutic strategies.
DOI: 10.57760/sciencedb.20797
title: A 30-meter spatial resolution dataset of ecosystem services in China for 2000, 2010, and 2020
title: A 30-meter spatial resolution dataset of ecosystem services in China for 2000, 2010, and 2020

description: The dataset is provided in raster format (GeoTIFF), Each folder represents a different ecosystem service (e.g., NPP, soil conservation). The file naming convention incorporates time period, and ecosystem service type to facilitate easy data organization and filtering. Spatially, the data is represented at a 30-meter resolution, while temporally, it covers 2000, 2010, and 2020.This dataset is applicable to a variety of scenarios, especially those that require detailed analysis of large areas. For example, in the fields of urbanization monitoring, nature reserve management, mineral resource development planning, and solar energy potential assessment across the country, this dataset can provide high-precision and comprehensive support for research. In addition, for the study of long-term changes, this dataset also shows its advantages, and can effectively capture and analyze the changing trends of land use and ecosystem services over the years. Through its adaptability to a variety of complex and dynamic environments, this dataset provides an ideal data foundation and analysis tool for a wide range of research and application scenarios.
description: The dataset is provided in raster format (GeoTIFF), Each folder represents a different ecosystem service (e.g., NPP, soil conservation). The file naming convention incorporates time period, and ecosystem service type to facilitate easy data organization and filtering. Spatially, the data is represented at a 30-meter resolution, while temporally, it covers 2000, 2010, and 2020.This dataset is applicable to a variety of scenarios, especially those that require detailed analysis of large areas. For example, in the fields of urbanization monitoring, nature reserve management, mineral resource development planning, and solar energy potential assessment across the country, this dataset can provide high-precision and comprehensive support for research. In addition, for the study of long-term changes, this dataset also shows its advantages, and can effectively capture and analyze the changing trends of land use and ecosystem services over the years. Through its adaptability to a variety of complex and dynamic environments, this dataset provides an ideal data foundation and analysis tool for a wide range of research and application scenarios.
DOI: 10.57760/sciencedb.31366
title: Sentiment classification of Indonesian financial news article titles
title: Sentiment classification of Indonesian financial news article titles

description: It contains Indonesian financial news from sources like CNBC and Detik, labeled with “negatif (negative),” “netral (neutral),” and “positif (positive).”  It is a financial sentiment analysis dataset (IndoFinSent) by scraping financial news article titles that are referring to a financial institution in Indonesia from the following sources, namely, Liputan6, CNBC, Detik, Sindo, Jawapos, and MetrotvNews. Each of the sentences (news titles) was manually labeled by 2 annotators and cross-checked by these annotators. In sum, we collect and label 2,274 entries in our dataset. The annotation took about 38 hours.
description: It contains Indonesian financial news from sources like CNBC and Detik, labeled with “negatif (negative),” “netral (neutral),” and “positif (positive).”  It is a financial sentiment analysis dataset (IndoFinSent) by scraping financial news article titles that are referring to a financial institution in Indonesia from the following sources, namely, Liputan6, CNBC, Detik, Sindo, Jawapos, and MetrotvNews. Each of the sentences (news titles) was manually labeled by 2 annotators and cross-checked by these annotators. In sum, we collect and label 2,274 entries in our dataset. The annotation took about 38 hours.
DOI: 10.57760/sciencedb.31362
title: Malaysia Financial Phrase Bank for financial sentiment analysis
title: Malaysia Financial Phrase Bank for financial sentiment analysis

description: It is translated and expert reviewed from FPB, a human-annotated phrase bank for financial and economic texts, into Malay, with labels as “negatif (negative),” “neutral (netral),” or “positif (positive).”
description: It is translated and expert reviewed from FPB, a human-annotated phrase bank for financial and economic texts, into Malay, with labels as “negatif (negative),” “neutral (netral),” or “positif (positive).”
DOI: 10.57760/sciencedb.31383
title: Financial stock prediction by Twitter and historical stock price data
title: Financial stock prediction by Twitter and historical stock price data

description: It utilizes historical stock price data to forecast the closing price change rate, categorized as: “Rise” (&gt;0.50%), “Fall” (&lt;-0.50%), and “Hold” (other cases).  There are two main components in our dataset: a Twitter dataset and a historical price dataset.
description: It utilizes historical stock price data to forecast the closing price change rate, categorized as: “Rise” (&gt;0.50%), “Fall” (&lt;-0.50%), and “Hold” (other cases).  There are two main components in our dataset: a Twitter dataset and a historical price dataset.
DOI: 10.57760/sciencedb.21713
title: Viruses as ecological architects in global wastewater treatment plants
title: Viruses as ecological architects in global wastewater treatment plants

description: Wastewater treatment plants (WWTPs) are critical yet underexplored hubs of viral diversity and activity. Through an unprecedented global-scale metagenomic analysis of 828 activated sludge samples spanning six continents, we unveil the Global Wastewater Activated Sludge Virus Catalog (GWASVC), comprising 140,190 non-redundant viral operational taxonomic units (vOTUs)—a 7.5-fold expansion of known WWTP viral diversity, with 80% representing unclassified lineages.
description: Wastewater treatment plants (WWTPs) are critical yet underexplored hubs of viral diversity and activity. Through an unprecedented global-scale metagenomic analysis of 828 activated sludge samples spanning six continents, we unveil the Global Wastewater Activated Sludge Virus Catalog (GWASVC), comprising 140,190 non-redundant viral operational taxonomic units (vOTUs)—a 7.5-fold expansion of known WWTP viral diversity, with 80% representing unclassified lineages.
DOI: 10.57760/sciencedb.31389
title: NLP-based stock prediction in Chinese
title: NLP-based stock prediction in Chinese

description: It features stock news and factors in China’s A-shares market, classified into “表现不佳(underperforming),” “跑赢大盘 (outperforming the market),” and “中性 (neutral).” It is featured by (1) a large number of stocks and stock-relevant news. (2) Availability of various financial factors. (3) Financially relevant metrics for evaluation. 
description: It features stock news and factors in China’s A-shares market, classified into “表现不佳(underperforming),” “跑赢大盘 (outperforming the market),” and “中性 (neutral).” It is featured by (1) a large number of stocks and stock-relevant news. (2) Availability of various financial factors. (3) Financially relevant metrics for evaluation.
DOI: 10.57760/sciencedb.31361
title: Social Text-Driven Stock Prediction in Indonesian
title: Social Text-Driven Stock Prediction in Indonesian

description: It is an Indonesian dataset for predicting stock trends by crawling stock prices from Yahoo Finance and social texts from Twitter using Tweepy. It is manually reviewed from the CIKM18 ChatGPT-translated dataset, predicting stocks based on Twitter news, labeled “naik (rise)” or “turun (fall)”. 
description: It is an Indonesian dataset for predicting stock trends by crawling stock prices from Yahoo Finance and social texts from Twitter using Tweepy. It is manually reviewed from the CIKM18 ChatGPT-translated dataset, predicting stocks based on Twitter news, labeled “naik (rise)” or “turun (fall)”.
DOI: 10.57760/sciencedb.31357
title: Financial stock prediction in Filipino
title: Financial stock prediction in Filipino

description: It is manually reviewed from the ACL18 ChatGPT-translated dataset, using historical stock data and Twitter texts to predict whether a stock’s closing price will “pagtaas (rise)” or “pagbagsak (fall)” on a given date.
description: It is manually reviewed from the ACL18 ChatGPT-translated dataset, using historical stock data and Twitter texts to predict whether a stock’s closing price will “pagtaas (rise)” or “pagbagsak (fall)” on a given date.
DOI: 10.57760/sciencedb.31382
title: Bullet Point Summarization of Long Earnings Call Transcripts
title: Bullet Point Summarization of Long Earnings Call Transcripts

description: It is an English dataset for text summarization. It focuses on generating fitting news headlines via abstractive summarization. It is a dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and expert-written short telegram-style bullet point summaries derived from corresponding Reuters articles. 
description: It is an English dataset for text summarization. It focuses on generating fitting news headlines via abstractive summarization. It is a dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and expert-written short telegram-style bullet point summaries derived from corresponding Reuters articles.
DOI: 10.6084/m9.figshare.30539543
title: <b>Optimizing Task Scheduling and Containers in Cloud Data Centers</b>

description: <b>Summary:</b><br>This study introduces <b>TPMCD (Throughput and Cost Optimizing Method for Clustering Tasks and Hybrid Containers in Cloud Data Centers)</b> — a novel approach designed to improve cloud efficiency, reduce costs, and balance workloads. By integrating metrics such as response time, execution accuracy, and sensitivity rate, TPMCD intelligently classifies and re-clusters tasks across virtual machines (VMs) and containers. This hybrid scheduling strategy minimizes redundancy, optimizes energy consumption, and ensures stability under dynamic workloads. Compared to existing algorithms, TPMCD achieved up to <b>7% cost reduction</b>, <b>4% throughput improvement</b>, and <b>9.5% faster real execution time</b>, while also using fewer computational nodes.<b>Context and Innovation:</b><br>In modern cloud computing, efficient resource allocation and energy optimization are critical challenges. Traditional scheduling often struggles with load imbalance, resource waste, and high management overhead from virtual machines. TPMCD addresses these issues through a synergy of clustering techniques and intelligent resource mapping between VMs and containers. By considering service-level agreements (SLAs) and adaptive thresholds, the method ensures high-quality performance, reliability, and reduced environmental impact. Overall, TPMCD provides a scalable and cost-efficient framework that enhances both <b>performance and sustainability</b> in cloud data centers.<code>Original article DOI: https://doi.org/10.1016/j.jnca.2025.104132</code>
DOI: 10.6084/m9.figshare.30539543.v1
title: <b>Optimizing Task Scheduling and Containers in Cloud Data Centers</b>

description: <b>Summary:</b><br>This study introduces <b>TPMCD (Throughput and Cost Optimizing Method for Clustering Tasks and Hybrid Containers in Cloud Data Centers)</b> — a novel approach designed to improve cloud efficiency, reduce costs, and balance workloads. By integrating metrics such as response time, execution accuracy, and sensitivity rate, TPMCD intelligently classifies and re-clusters tasks across virtual machines (VMs) and containers. This hybrid scheduling strategy minimizes redundancy, optimizes energy consumption, and ensures stability under dynamic workloads. Compared to existing algorithms, TPMCD achieved up to <b>7% cost reduction</b>, <b>4% throughput improvement</b>, and <b>9.5% faster real execution time</b>, while also using fewer computational nodes.<b>Context and Innovation:</b><br>In modern cloud computing, efficient resource allocation and energy optimization are critical challenges. Traditional scheduling often struggles with load imbalance, resource waste, and high management overhead from virtual machines. TPMCD addresses these issues through a synergy of clustering techniques and intelligent resource mapping between VMs and containers. By considering service-level agreements (SLAs) and adaptive thresholds, the method ensures high-quality performance, reliability, and reduced environmental impact. Overall, TPMCD provides a scalable and cost-efficient framework that enhances both <b>performance and sustainability</b> in cloud data centers.<code>Original article DOI: https://doi.org/10.1016/j.jnca.2025.104132</code>
DOI: 10.57760/sciencedb.31380
title: Generation of summaries for financial news
title: Generation of summaries for financial news

description: It is known as the FinNA dataset, consisting of financial news articles along with their abstracts. 
description: It is known as the FinNA dataset, consisting of financial news articles along with their abstracts.
DOI: 10.57760/sciencedb.31342
title: Financial text summarization of Indonesian news URLs and headlines
title: Financial text summarization of Indonesian news URLs and headlines

description: It is a Indonesia dataset for financial text summarization, including Indonesian news URLs and headlines from detik.com, capable of generating similar titles from the URL content.
description: It is a Indonesia dataset for financial text summarization, including Indonesian news URLs and headlines from detik.com, capable of generating similar titles from the URL content.
DOI: 10.57760/sciencedb.31339
title: Generation of summaries for financial news in Thai
title: Generation of summaries for financial news in Thai

description: It is derived from the FinNA dataset, a generation dataset of summaries for financial news, translated into Thai through ChatGPT and reviewed by experts. The input of the dataset is a financial news segment, and the model needs to generate a sentence summary.
description: It is derived from the FinNA dataset, a generation dataset of summaries for financial news, translated into Thai through ChatGPT and reviewed by experts. The input of the dataset is a financial news segment, and the model needs to generate a sentence summary.
DOI: 10.57760/sciencedb.31381
title: Financial text classification of Headlines
title: Financial text classification of Headlines

description: It contains gold-related news headlines from 2000 to 2019 annotated with nine categories like “price up”, “price down”, “price stable”, “future price”, “as set comparisons” and others. 
description: It contains gold-related news headlines from 2000 to 2019 annotated with nine categories like “price up”, “price down”, “price stable”, “future price”, “as set comparisons” and others.
DOI: 10.57760/sciencedb.31390
title: Multi-label classification of financial news
title: Multi-label classification of financial news

description: It utilizes the FinNL dataset, sourced from financial news websites, with labels like “股票市场 (stock market)”, “外汇 (foreign exchange)”, “宏观经济 (macroeconomics)”, containing both binary (NL) and multi-class (NL2) data.
description: It utilizes the FinNL dataset, sourced from financial news websites, with labels like “股票市场 (stock market)”, “外汇 (foreign exchange)”, “宏观经济 (macroeconomics)”, containing both binary (NL) and multi-class (NL2) data.
DOI: 10.57760/sciencedb.31363
title: Multi-label classification of financial news in Vietnamese
title: Multi-label classification of financial news in Vietnamese

description: It is a ChatGPT-based translations into Vietnamese, reviewed by experts from the NL dataset.
description: It is a ChatGPT-based translations into Vietnamese, reviewed by experts from the NL dataset.
DOI: 10.15468/dl.9ffxjw
title: Occurrence Download

description: A dataset containing 898 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bat droppings DNA survey in attics of historic buildings, Flanders, Belgium"
 ]
}

 The dataset includes 898 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005179-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.31488
title: Soil Enzyme Activity of an Artificially Seeded Grassland in an Alpine Coalmine Field
title: Soil Enzyme Activity of an Artificially Seeded Grassland in an Alpine Coalmine Field

description: This dataset primarily contains soil enzyme activity data from artificially seeded grasslands on the spoil heaps of an alpine mine. The data covers both shady and sunny slopes under different land use types. Specifically, columns 1-8 represent data from the sunny slopes, and columns 10-17 represent data from the shady slopes. In both sections, Column 1 and Column 10 indicate the treatment types, while Column 2 and Column 11 list the sample IDs.
description: This dataset primarily contains soil enzyme activity data from artificially seeded grasslands on the spoil heaps of an alpine mine. The data covers both shady and sunny slopes under different land use types. Specifically, columns 1-8 represent data from the sunny slopes, and columns 10-17 represent data from the shady slopes. In both sections, Column 1 and Column 10 indicate the treatment types, while Column 2 and Column 11 list the sample IDs.
DOI: 10.5281/zenodo.2653022
title: S33 | SOLUTIONSMLOS | Chemicals used for Modelling in SOLUTIONS

description: SOLUTIONSMLOS contains the chemicals used for modelling in the SOLUTIONS project (www.solutions-project.eu/), provided by Jaroslav Slobodnik (EI). Dataset DOI: 10.5281/zenodo.2653022.

Update 14 Nov 2019: added CSV file. 6 Feb. 2020 CSV with corrected SMILES entries for PubChem import. 6 Nov 2020 structure fix for CAS 111360-16-8 (reported by Leon, PubChem). 17 July 2022: more SMILES fixes, plus one InChIKey change. 18 June 2023: one more SMILES fix (YLMOTKLYENPQLK-VMPITWQZSA-N) in CSV only.
description: This is the work of many SOLUTIONS colleagues. This dataset is in an uncurated state
DOI: 10.5281/zenodo.8052416
title: S33 | SOLUTIONSMLOS | Chemicals used for Modelling in SOLUTIONS

description: SOLUTIONSMLOS contains the chemicals used for modelling in the SOLUTIONS project (www.solutions-project.eu/), provided by Jaroslav Slobodnik (EI). Dataset DOI: 10.5281/zenodo.2653022.

Update 14 Nov 2019: added CSV file. 6 Feb. 2020 CSV with corrected SMILES entries for PubChem import. 6 Nov 2020 structure fix for CAS 111360-16-8 (reported by Leon, PubChem). 17 July 2022: more SMILES fixes, plus one InChIKey change. 18 June 2023: one more SMILES fix (YLMOTKLYENPQLK-VMPITWQZSA-N) in CSV only.
description: This is the work of many SOLUTIONS colleagues. This dataset is in an uncurated state