cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25405/data.ncl.29968042.v2
title: Quasar Feedback Survey 2: Set of figures of radio maps

description: This data release is associated with the publication "The Quasar Feedback Survey: Revealing the importance of sensitive radio imaging deeper into the radio quiet regime", published in Njeri et al. (2026), MNRAS, under review.This work presents 1.4GHz and 6 GHz radio imaging, obtained from the Very Large Array for the 29 quasars from the extended sample, to lower luminosities (QFeedS-2), of the Quasar Feedback Survey (first presented in Jarvis et al. 2021, MNRAS, 503, 1780 https://doi.org/10.1093/mnras/stab549 ). These targets are z~0.1 quasar host galaxies, selected to be luminous in [O III] emission (L[O III] &gt; 10<sup>42.1</sup><sup> </sup>erg/s). For full details of the survey description and sample selection see Njeri et al. (2026).This data release contains a set of figures for each of the 29 sources. These are equivalent to the examples shown in the primary paper (Njeri et al. 2025) for three example galaxies. The rgb images come from the DESI Legacy Imaging Survey in the 𝑧, 𝑟 , 𝑔 bands. The contours overlaid represent: FIRST maps with levels 1𝜎 × [2, 3, 4, 5]; our new VLA 1.4 GHz maps with levels 1𝜎 × [2, 4, 6, 8] and our new VLA 6 GHz maps with levels 1𝜎 × [2, 4, 6, 8]. Ellipses represent the synthesised beam for the corresponding radio map contours; ∼5 arcsec for FIRST (in red), ∼1 arcsec for VLA 1.4 GHz (in green) and ∼0.3 arcsec for VLA 6 GHz (in blue). The scale bar highlights the physical size scales.Our VLA images produced from the Very Large Array (VLA) data, using the A-array configuration. The raw VLA data were obtained under proposal ID 23A-214. We used the VLA Calibration Pipeline 2024.1.0.8 for casa version 6.6.1 to reduce our data. Imaging was performed using the casa tclean algorithm with Briggs weighting of r=0.5 for all radio maps.
DOI: 10.17026/ar/hqfq1m
title: PAN-00159250 - late medieval broad arrow head

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00159250&lt;/p&gt;
description: Modified: 2025-09-23T13:51:34+02:00
DOI: 10.5281/zenodo.17787143
title: The Role of Lecturer–Student Interaction in Developing Critical Thinking Skills: Insights from a Private University in Indonesia

description: This repository contains all datasets supporting the article “The Role of Lecturer–Student Interaction in Developing Critical Thinking Skills: Insights from a Private University in Indonesia”.

The dataset includes:- Interview transcripts (Bahasa Indonesia)- Thematic coding files- Raw qualitative notes- Values used to generate the figures presented in the article
DOI: 10.6084/m9.figshare.30761027
title: Transcriptomic Analysis of Murine Skin Wound Healing Treated with American Cockroaches Nanovesicles

description: This project investigates the molecular mechanisms underlying skin wound healing in mice following treatment with fresh nanovesicles derived from American cockroach. Using RNA sequencing, we identified differentially expressed genes (DEGs) associated with wound repair processes and performed functional annotation to uncover key biological pathways and regulatory networks. The dataset includes quality control reports, alignment statistics, gene expression profiles, DEG lists, and functional enrichment results. This work provides insights into the therapeutic potential of insect-derived EVs in regenerative medicine.
DOI: 10.6084/m9.figshare.30761027.v1
title: Transcriptomic Analysis of Murine Skin Wound Healing Treated with American Cockroaches Nanovesicles

description: This project investigates the molecular mechanisms underlying skin wound healing in mice following treatment with fresh nanovesicles derived from American cockroach. Using RNA sequencing, we identified differentially expressed genes (DEGs) associated with wound repair processes and performed functional annotation to uncover key biological pathways and regulatory networks. The dataset includes quality control reports, alignment statistics, gene expression profiles, DEG lists, and functional enrichment results. This work provides insights into the therapeutic potential of insect-derived EVs in regenerative medicine.
DOI: 10.6084/m9.figshare.30761027.v2
title: Transcriptomic Analysis of Murine Skin Wound Healing Treated with American Cockroaches Nanovesicles

description: This project investigates the molecular mechanisms underlying skin wound healing in mice following treatment with fresh nanovesicles derived from American cockroach. Using RNA sequencing, we identified differentially expressed genes (DEGs) associated with wound repair processes and performed functional annotation to uncover key biological pathways and regulatory networks. The dataset includes quality control reports, alignment statistics, gene expression profiles, DEG lists, and functional enrichment results. This work provides insights into the therapeutic potential of insect-derived EVs in regenerative medicine.
DOI: 10.26301/jw7s-jk65
title: Çatalhöyük - East Mound North Area 2012

description: This collection includes a mixed remote sensing dataset made of terrestrial laser scanning point clouds, point cloud comparison data, structure from motion-generated polygonal meshes, and related textures and metadata. It was recorded at the Neolithic site of Çatalhöyük, Turkey under the Çatalhöyük Digital Preservation Project carried out in 2012-2017 by scholars and students from the University of California Merced and Cardiff University. The historic series of North Area and South Area point clouds were documented by means of a Faro Focus S120 time of flight laser scanning during six consecutive field seasons (2012-2017), while the East Mound and West Mound landscape 3-D models were generated from low-altitude photos captured by an unmanned aerial vehicle (drone) in 2015.

The goal of the Çatalhöyük Digital Preservation Project was to capture the current state of the site to comply with UNESCO site management guidelines for conservation and to understand and quantitatively assess the deterioration of its mud-brick architecture through time via 3-D data comparison and geo spatial methods. The analyses of laser scanning data focus on the wall features of eight buildings of the North Area (B5, B48, B49, B55, B64, B82, B114, B119), which were chosen as priority by Ashley Lingle, the Head of Conservation at Çatalhöyük. Point clouds of wall features were segmented and aligned using the open source software CloudCompare. Pairs of perfectly aligned and identically segmented point clouds were then compared using the M3C2 method in CloudCompare to compute differences (standard deviation) and significant change. The differences among aligned and identical features represent the material loss occurred in those walls in 2012-2017. The point clouds were then analyzed in a GIS platform including environmental data logged in the North Area and vulnerability assessment made in situ by the Conservation Team.
description: Çatalhöyük (Turkish pronunciation: also Çatal Höyük and Çatal Hüyük; from Turkish Çatal 'fork' + höyük 'tumulus') was a very large Neolithic and Chalcolithic proto-city settlement in southern Anatolia, which existed from approximately 7100 BC to 5700 BC, and flourished around 7000 BC.[2] In July 2012, it was inscribed as a UNESCO World Heritage Site.

Çatalhöyük is located overlooking the Konya Plain, southeast of the present-day city of Konya (ancient Iconium) in Turkey, approximately 140 km (87 mi) from the twin-coned volcano of Mount Hasan. The eastern settlement forms a mound which would have risen about 20 m (66 ft) above the plain at the time of the latest Neolithic occupation. There is also a smaller settlement mound to the west and a Byzantine settlement a few hundred meters to the east. The prehistoric mound settlements were abandoned before the Bronze Age. A channel of the ÇarÅŸamba River once flowed between the two mounds, and the settlement was built on alluvial clay which may have been favorable for early agriculture.

Source: https://en.wikipedia.org/wiki/%C3%87atalh%C3%B6y%C3%BCk
description: LiDAR - Terrestrial
DOI: 10.17026/ar/mdo2xr
title: PAN-00047870 - monochrome glass bead barrel/spherical shaped (antique)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00047870&lt;/p&gt;
description: Modified: 2025-08-08T17:31:22+02:00
DOI: 10.5281/zenodo.17235778
title: Data for Critical fluctuations and conserved dynamics in a strange ferromagnetic metal
DOI: 10.5281/zenodo.17235777
title: Data for Critical fluctuations and conserved dynamics in a strange ferromagnetic metal
DOI: 10.17026/ar/xzozhw
title: PAN-00177165 - button with solid convex head and flat base, loop-shaped shank, decorated

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00177165&lt;/p&gt;
description: Modified: 2025-08-07T09:28:50+02:00
DOI: 10.5281/zenodo.17787500
title: COVID-19-Todesfälle in Deutschland

description: Im Datensatz "COVID-19-Todesfälle in Deutschland" des Robert Koch-Instituts werden täglich aktualisierte Informationen zu COVID-19-bedingten Todesfällen in Deutschland bereitgestellt. Die Datenerhebung erfolgt im Rahmen des Infektionsschutzgesetzes (IfSG). Der Datensatz umfasst aggregierte Todesfallzahlen nach Berichtsdatum, Altersgruppen und Bundesländern und wird ergänzt durch den Fall-Verstorbenen-Anteil. Der Datensatz ermöglicht fundierte Analysen zur Bewertung der Krankheitsschwere und Verbreitung von COVID-19 in Deutschland.
DOI: 10.17026/ar/z5lknt
title: PAN-00131592 - iron spearhead/lancehead

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00131592&lt;/p&gt;
description: Modified: 2025-08-29T11:06:03+02:00
DOI: 10.13016/hiry-dja5
title: Solid-State Battery Database Mastersheet

description: The data in this database was extracted from published solid-state battery papers. For more information on the data extraction methods please contact fpetrac@umd.edu.
description: Despite thousands of annual publications on battery devices, standard reporting practices are distinctly absent, resulting in a wide variation of reported data that is difficult to search and analyze. To address this, we constructed a database of research-level solid-state batteries with a preliminary ontological framework reflected in 167 columns and populated with 245 batteries resulting in over 40,000 data-points. This project’s significance is threefold: (1) Our variables include materials, components, fabrication, and cell testing, whose combination is essential to describe and properly assess a solid-state battery. (2) Our inclusion of “not applicable” and “not reported” values allow us to analyze reporting practices and gain insights on ”missing data” in battery publications. (3) We present a structure for battery data with well-defined variables, facilitating formatted reporting. Key applications include a streamlined visualization and search workflow employing numerical and categorical criteria, quantitative analysis of reporting practices, a training set for AI paper-data extraction, and connections with existing battery ontologies.
DOI: 10.34856/eos-rs_202509_philippines_tropical_storm_bualoi_floods
title: EOS-RS Flood Proxy Map: Mindoro, Philippines, Floods, 27 Sep 2025

description: The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Mindoro, Philippines, due to tropical storm Bualoi. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (12 Apr 2025 to 21 Sep 2025) and after (27 Sep 2025) the event.
DOI: 10.34740/kaggle/dsv/6056343
title: Bank BSI Ransomware Tweets - 24k Tweets
DOI: 10.17026/ar/d083vo
title: PAN-00177847 - symmetrical buckle with simple integral bar and two oval frames Group D

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00177847&lt;/p&gt;
description: Modified: 2025-08-05T13:46:11+02:00
DOI: 10.5281/zenodo.17787510
title: https://522bet1.uk.com/

description: Recomendo 522bet, casa de apostas online segura, com bônus atraentes, pagamentos rápidos e ótimas odds para fãs de esportes.

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DOI: 10.5281/zenodo.17787512
title: https://522bet1.uk.com/

description: Recomendo 522bet, casa de apostas online segura, com bônus atraentes, pagamentos rápidos e ótimas odds para fãs de esportes.

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DOI: 10.5281/zenodo.17787071
title: MoMA Collection - Automatic Update

description: Temporary Description
DOI: 10.5281/zenodo.17787070
title: MoMA Collection - Automatic Update

description: Temporary Description
DOI: 10.5281/zenodo.17787440
title: ANALISIS PERSEPSI PENGGUNA ATAS TEKNOLOGI SENSESCAPING MELALUI FITUR VIRTUAL TRY ON DALAM INDUSTRI KOSMETIK LOKAL

description: Data ini berisi hasil mentah (raw data) dari survei yang dilakukan untuk menguji Technology Acceptance Model (TAM), UTAUT)

Tujuan Penelitian

Tujuan utama penelitian ini adalah untuk mengukur dan menganalisis faktor-faktor yang mempengaruhi Niat Perilaku (Behavioral Intention) atau Penggunaan (Usage)] terhadap  PENGGUNA ATAS TEKNOLOGI SENSESCAPING MELALUI FITUR VIRTUAL TRY ON DALAM INDUSTRI KOSMETIK LOKAL

Metode Pengumpulan Data





Populasi dan Sampel: Responden diambil dari pengguna aktif aplikasi X, mahasiswa, karyawan dengan total $N = 344 setelah melalui tahap screening




Instrumen: Kuesioner ini dikembangkan berdasarkan Skala Likert 5 Poin (1=Sangat Tidak Setuju, 5=Sangat Setuju)].




Data disimpan dalam format CSV. Setiap baris mewakili respon dari satu responden.
DOI: 10.5281/zenodo.17787441
title: ANALISIS PERSEPSI PENGGUNA ATAS TEKNOLOGI SENSESCAPING MELALUI FITUR VIRTUAL TRY ON DALAM INDUSTRI KOSMETIK LOKAL

description: Data ini berisi hasil mentah (raw data) dari survei yang dilakukan untuk menguji Technology Acceptance Model (TAM), UTAUT)

Tujuan Penelitian

Tujuan utama penelitian ini adalah untuk mengukur dan menganalisis faktor-faktor yang mempengaruhi Niat Perilaku (Behavioral Intention) atau Penggunaan (Usage)] terhadap  PENGGUNA ATAS TEKNOLOGI SENSESCAPING MELALUI FITUR VIRTUAL TRY ON DALAM INDUSTRI KOSMETIK LOKAL

Metode Pengumpulan Data





Populasi dan Sampel: Responden diambil dari pengguna aktif aplikasi X, mahasiswa, karyawan dengan total $N = 344 setelah melalui tahap screening




Instrumen: Kuesioner ini dikembangkan berdasarkan Skala Likert 5 Poin (1=Sangat Tidak Setuju, 5=Sangat Setuju)].




Data disimpan dalam format CSV. Setiap baris mewakili respon dari satu responden.
DOI: 10.5281/zenodo.12700065
title: Code4ML 2.0: a Large-scale Dataset of annotated Machine Learning Code

description: This is an enriched version of Code4ML: a Large-scale Dataset of annotated Machine Learning Code, a corpus of Python code snippets, competition summaries, and data descriptions from Kaggle. The initial corpus consists of ≈ 2.5 million snippets of ML code collected from ≈ 100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. 

The data is organized as a set of tables in CSV format. It includes several central entities: raw code blocks collected from Kaggle (code_blocks.csv), kernels (kernels_meta.csv) and competitions meta information (competitions_meta.csv). Manually annotated code blocks are presented as a separate table (murkup_data.csv). As this table contains the numeric id of the code block semantic type, we also provide a mapping from the id to semantic class and subclass (vertices.csv).

Snippets information (code_blocks.csv) can be mapped with kernels meta-data via kernel_id. Kernels metadata is linked to Kaggle competitions information through comp_name. To ensure the quality of the data kernels_meta.csv includes only notebooks with an available Kaggle score.

Automatic classification of code_blocks are stored in data_with_preds.csv. The mapping of this table with code_blocks.csv can be doe through code_blocks_index column, which corresponds to code_blocks indices.

The updated Code4ML 2.0 corpus includes kernels retrieved from Code Kaggle Meta. These kernels correspond to the kaggle competitions launched since 2020. The natural descriptions of the competitions are retrieved with the aim of LLM. 

kernels_meta2.csv may contain kernels without Kaggle score, but with the place in the leader board (rank).

Code4ML 2.0 dataset can be used for various purposes, including training and evaluating models for code generation, code understanding, and natural language processing tasks.
DOI: 10.57760/sciencedb.j00133.00582
title: Dermatosis Retrieval Prototype System and Experimental Dataset
title: 皮肤病检索原型系统及实验数据集

description: Contains processed multi-source data and prototype system pages. The multi-source data includes all skin disease datasets from the Q&A website and the DermNet NZ online dermatology resource platform, as well as the vitiligo special dataset from Youlai Doctor Network, and data nodes and relationship data from a multimodal knowledge graph. 
description: Contains processed multi-source data and prototype system pages. The multi-source data includes all skin disease datasets from the Q&A website and the DermNet NZ online dermatology resource platform, as well as the vitiligo special dataset from Youlai Doctor Network, and data nodes and relationship data from a multimodal knowledge graph.
DOI: 10.57760/sciencedb.j00133.00578
title: Few-shot NOTA data after AKAP preprocessing
title: AKAP预处理后的少样本NOTA数据

description: By referring to the data conversion algorithm from https://github.com/clulab/releases/tree/master/lrec2024-realistic-fewshot-meta-dataset for the Fewrel and Few-shot tacred datasets, perform NOTA data conversion to fit real-world scenarios.
description: By referring to the data conversion algorithm from https://github.com/clulab/releases/tree/master/lrec2024-realistic-fewshot-meta-dataset for the Fewrel and Few-shot tacred datasets, perform NOTA data conversion to fit real-world scenarios.
DOI: 10.17026/ar/qsbeqa
title: PAN-00000629 - strap end group 10.2

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00000629&lt;/p&gt;
description: Modified: 2025-08-12T15:13:13+02:00
DOI: 10.57760/sciencedb.j00133.00577
title: The training dataset, as well as the raw data and source code of all comparative experiments and ablation experiments conducted on the two datasets.
title: 训练数据集以及在两个数据集上进行的所有对比实验以及消融实验原始数据和源代码

description: This dataset consists of three parts, one part is the public dataset containing all original images, annotated data, and my own newly added annotation data; The other two parts are the raw data and source code of all comparative experiments and ablation experiments conducted on two datasets, respectively 
description: This dataset consists of three parts, one part is the public dataset containing all original images, annotated data, and my own newly added annotation data; The other two parts are the raw data and source code of all comparative experiments and ablation experiments conducted on two datasets, respectively
DOI: 10.57760/sciencedb.30278
title: Large-scale AI Terminology-related Dataset
title: 大规模人工智能术语相关数据集

description: This dataset comprises the following four sub-datasets:(1) The complete set of AI terminology extracted by heterogeneous agents and their metric measurements;(2) Expert validation results on the ratings of sampled AI terminology;(3) A high-quality labeled training set of AI terminology (for fine-tuning SciBERT);(4) New AI terminology mined by the fine-tuned SciBERT.
description: This dataset comprises the following four sub-datasets:(1) The complete set of AI terminology extracted by heterogeneous agents and their metric measurements;(2) Expert validation results on the ratings of sampled AI terminology;(3) A high-quality labeled training set of AI terminology (for fine-tuning SciBERT);(4) New AI terminology mined by the fine-tuned SciBERT.
DOI: 10.57760/sciencedb.j00133.00574
title: Datasets for LLM Prompt-Based Multi-Modal Encoding Method for Chinese Hate Speech Detection
title: 基于LLM提示的多模态编码中文仇恨言论检测方法数据集

description: This dataset includes two Chinese hate speech detection datasets, namely Coldataset and CHSD, which are used to test the effectiveness of detection methods. Additionally, it comprises the datasets and models used for training and validation in this study, respectively in the processes of sentiment analysis and Pinyin-glyph feature encoding. In addition, it also includes the foundation models applied in the comparative experiment phase.
description: This dataset includes two Chinese hate speech detection datasets, namely Coldataset and CHSD, which are used to test the effectiveness of detection methods. Additionally, it comprises the datasets and models used for training and validation in this study, respectively in the processes of sentiment analysis and Pinyin-glyph feature encoding. In addition, it also includes the foundation models applied in the comparative experiment phase.
DOI: 10.57760/sciencedb.j00133.00571
title: The experimental dataset
title: The experimental dataset

description: This dataset includes the following four files:Code Implementation and Evaluation Metrics Computation(code file): Comprehensive source code for model implementation and calculation of performance evaluation metrics, including accuracy, precesion, recall, and F1.Sample Dataset (data file): Complete dataset comprising training, validation, and test subsets.Trained model and Experimental Results (result file): Fully trained model and comprehensive experimental outcomes including performance metrics, and comparative validation data.High-Resolution Visual Documentation (picture file): Collection of high-resolution graphical representations, including AUC curves, Taylor curves, and others.
description: This dataset includes the following four files:Code Implementation and Evaluation Metrics Computation(code file): Comprehensive source code for model implementation and calculation of performance evaluation metrics, including accuracy, precesion, recall, and F1.Sample Dataset (data file): Complete dataset comprising training, validation, and test subsets.Trained model and Experimental Results (result file): Fully trained model and comprehensive experimental outcomes including performance metrics, and comparative validation data.High-Resolution Visual Documentation (picture file): Collection of high-resolution graphical representations, including AUC curves, Taylor curves, and others.
DOI: 10.57760/sciencedb.j00133.00563
title: Henan heavy rain incident, Japan's nuclear wastewater discharge incident, Israel-Palestine conflict incident dataset
title: 河南暴雨事件，日本排放核污染水事件，巴以冲突事件数据集

description: Original data of public opinion on three sudden incidents 
description: Original data of public opinion on three sudden incidents