cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/inscriptifact-ouc1sto1611885
title: InscriptiFact -- an image database of inscriptions and artifacts

description: The InscriptiFact Collection is designed to allow access via the Internet to high-resolution images of ancient inscriptions and artifacts, primarily from the Near Eastern and Mediterranean Worlds. The targeted inscriptions constitute some of the world’s earliest written records, which are mostly housed in a number of international museums and libraries, as well as field projects where inscriptions still remain in situ. Included, for example, are, selected Dead Sea Scrolls; cuneiform tablets from Mesopotamia, Persia, Canaan and Egypt; early Jewish papyri from Egypt; lapidary inscriptions, primarily from Jordan, Lebanon and Cyprus; inscriptions on a variety of hard media (e.g., ostraca, copper, semi-precious stones, jar handles) mostly written in early Canaanite, Phoenician, Hebrew, Aramaic, Ammonite and Edomite; and much more. These ancient texts are foundational documents that serve as a point of reference for Judaism, Christianity, Islam and the cultures out of which they emerged. The name "InscriptiFact" is intended to convey the concept of a scholarly archive based on "facts" about "inscriptions" and "artifacts." West Semitic Research Project (WSRP) under the auspices of the University of Southern California (USC) is broadly acknowledged as a leader in the application of photographic and digital technologies to capture and analyze data of ancient texts. The West Semitic Research image archive now contains approximately 1,500,000 images. Important! Search terms with more than one word or number need to be enclosed in quotation marks.Guide to Searching InscriptiFact
DOI: 10.25549/inscriptifact-ouc110626871
title: Persepolis Fortification Aramaic Text 712, PFAT 712

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAT 712 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with Aramaic text. Seal impressions on reverse and upper edge. ❧ [Physical Object Note:] Damage or cracks on left edge and upper edge.
DOI: 10.25549/inscriptifact-ouc110623562
title: Persepolis Fortification Anepigraphic Tablet 0884.201, PFAnep 0884.201

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAnep 0884.201, PFUT 0884-201 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay tablet with seal impressions on bottom edge, left edge, obverse, reverse and upper edge. ❧ [Physical Object Note:] Small black sots on surfaces of tablet. Small cracks on left edge, obverse, reverse.
DOI: 10.25549/inscriptifact-ouc110626873
title: Persepolis Fortification Anepigraphic Tablet 1091.101, PFAnep 1091.101

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAnep 1091.101, PFUT 1091-101 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay tablet with seal impressions on bottom edge, left edge, obverse, reverse and upper edge. ❧ [Physical Object Note:] Cracks on bottom edge, left edge, obverse, reverse, upper edge. Holes on bottom edge, left edge.
DOI: 10.25549/inscriptifact-ouc110623556
title: Persepolis Fortification Anepigraphic Tablet 1092.103, PFAnep 1092.103

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAnep 1092.103, PFUT 1092-103 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay tablet with seal impressions on bottom edge, left edge, obverse, reverse and upper edge. ❧ [Physical Object Note:] Small crack on bottom edge. Slight damage on upper edge. Some encrustation on obverse, reverse.
DOI: 10.25549/inscriptifact-ouc110626867
title: Persepolis Fortification Aramaic Text 711, PFAT 711

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAT 711 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with Aramaic text. Seal impressions on flat edge, obverse and reverse. ❧ [Physical Object Note:] Damage or cracks on all sides.
DOI: 10.25549/inscriptifact-ouc110623554
title: Persepolis Fortification Anepigraphic Tablet 0884.201, PFAnep 0884.201

description: [Text Description:] Administrative text with seals ❧ [Text or Publication Number Note:] PFAnep 0884.201, PFUT 0884-201 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay tablet with seal impressions on bottom edge, left edge, obverse, reverse and upper edge. ❧ [Physical Object Note:] Small black sots on surfaces of tablet. Small cracks on left edge, obverse, reverse.
DOI: 10.15468/dl.nrqdys
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cariamiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((20 20,30 20,30 30,20 30,20 20))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026041-251009101135966/datasets/export for details.
DOI: 10.15468/dl.b5mejj
title: Occurrence Download

description: A dataset containing 827924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United Kingdom of Great Britain and Northern Ireland, Netherlands, Sweden, Switzerland)",
 "TaxonKey is Noctua pronuba",
 "Year 2005-2024"
 ]
}

 The dataset includes 827924 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025980-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.akxqq6
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lolium temulentum",
 "Country is Pakistan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079050-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5zd4rb
title: Occurrence Download

description: A dataset containing 2992 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tragopan satyra"
 ]
}

 The dataset includes 2992 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079049-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tc5h2hfxhr.1
title: Capacitated Waste Collection Routing

description: This includes the MATLAB file (fixed_bins.mat) containing all bin coordinates and waste levelss used in the experiments. Algorithm implementations are available from the corresponding author upon request.
DOI: 10.17632/t99mbc7by9.1
title: Characteristics of carbon, nitrogen, and phosphorus in soil aggregates under different restoration methods on cut slopes in the Qinghai-Tibet Plateau mining area

description: This study systematically compared soil aggregate stability and carbon-nitrogen-phosphorus nutrient characteristics among three restoration methods for cut slopes (three-dimensional netting [TCS], frame beams [FCS], and galvanized wire mesh [GCS]) relative to unrestored cut slopes (UCS) in the Jiama Mining Area, Tibet. Microsoft Excel 2022 was used for data organization, while statistical analyses were conducted with SPSS 20.0. Statistical comparisons of restoration treatments for soil properties (nutrient indicators, enzyme activities) employed one-way ANOVA with Tukey's HSD multiple comparisons.
TCS exhibited significantly higher SOC content than FCS, GCS, and UCS. For aggregates smaller than 0.25 mm, FCS exhibited significantly higher SOC values than other restoration methods. While bulk soil SOC contents were significantly elevated in FCS and TCS compared to GCS and UCS, GCS and UCS did not differ significantly from each other. TCS exhibited significantly higher TN contents in most aggregate size classes compared to other treatments. Among the three restoration methods, only GCS exhibited lower TN contents than UCS across all aggregate size fractions. In bulk soil, TN contents in GCS and UCS showed no significant difference but were both significantly lower than those in TCS and FCS, with TCS also showing significantly higher TN content than FCS. A significant increase in TP content was observed in macroaggregates from TCS and FCS compared with GCS and UCS. Within the microaggregates fraction, TP concentrations were significantly elevated in FCS and UCS relative to TCS and GCS. TP concentrations in the &lt; 0.053 mm fraction were significantly elevated in FCS relative to other treatments. In bulk soil, UCS had the highest TP content, significantly higher than that in TCS and GCS.
TCS exhibited significantly higher soil saccharase (SAC) activity across all aggregate size fractions compared to the other cut slopes. In aggregates smaller than 2 mm, both FCS and GCS exhibited higher SAC activity than UCS. Statistical analysis detected no significant variation in bulk soil SAC activity between GCS and UCS treatments. Both TCS and FCS exhibited higher urease (URE) activity than GCS and UCS. UCS exhibited significantly higher URE activity than GCS only in the microaggregates fraction. TCS exhibited significantly higher alkaline phosphatase (ALP) activity than the other cut slopes. Except in the microaggregates fraction, where FCS, GCS, and UCS showed no significant differences in ALP activity. In bulk soil, ALP activity differed significantly among the four slope types, with the hierarchy: TCS &gt; FCS &gt; GCS &gt; UCS. Across bulk soil and &gt; 0.053 mm aggregates, catalase (CAT) activity was significantly lower in GCS compared to other slope types. TCS demonstrated significantly higher CAT activity than other restoration methods in both bulk soil and &lt; 2 mm aggregates, while FCS and GCS showed no significant difference.
DOI: 10.17632/9587tr53gt
title: FLG dataset for manuscript

description: This dataset is for the analysis of Scientific productivity at Indian central universities
DOI: 10.17632/9587tr53gt.1
title: FLG dataset for manuscript

description: This dataset is for the analysis of Scientific productivity at Indian central universities
DOI: 10.17632/mt7w6y8p9z.1
title: EST-SSR development from de novo transcriptome assembly of Typhonium flagelliforme

description: Title: Data set EST-SSR Primer of Typhonium flagelliforme
This data is an appendix to the publication of the proceedings paper at The 2nd International Seminar on Tropical Bioresources Advancement and Technology (ISOTOBAT 2025).
DOI: 10.17632/2pbtmsrwbg.1
title: Can congresses play a positive role in environmental governance?

description: We analyze panel data from 286 Chinese cities between 2003 and 2020 using a staggered difference-in-differences approach to assess whether and how budget legislation by people’s congresses impacts air quality.All original data is in the file named all_data.xlsx. the descriptive statistics is in the file named 0_Data processing and descriptive statistics.do. The left files are the modelling and processing of DID regression. Hypothesis 1 is that Budget legislation by people’s congresses facilitates environmental governance while Hypothesis 1 is that budget legislation by people’s congresses cannot enhance environmental governance effectiveness.
This study measures environmental governance levels based on air quality, using cities’ mean PM2.5 data obtained by matching NASA and MODIS satellite observations compiled by the Atmospheric Composition Analysis Group of the United States with China’s administrative division vector data.The core explanatory variable in this study is the dummy variable of the People’s Congress budget legislation. If the province to which the city belongs in the current year issues the corresponding budget supervision regulations in accordance with the Budget Law of the People’s Republic of China, it is assigned a value of 1; otherwise, it is assigned a value of 0. We find budget legislation significantly reduces particulate matter 2.5 (PM2.5) in the air by 1.58% and carbon emissions by 1.2% in pilot cities, and these findings are highly robust.This study is the first to systematically demonstrate the effectiveness of budget legislation as a tool for congressional environmental governance, an area that has previously neglected budgetary means. The findings highlight the need to enhance the budgetary authority of people’s congresses and ensure their proper roles in environmental governance.
DOI: 10.17632/thfx7cnt6w.1
title: FAKE NEWS IN THE DIGITAL ENVIRONMENT AND ITS NEGATIVE EFFECTS ON SOCIETY

description: In today's digital age, fake news is one of the most serious challenges to social security, politics and social relations. The rapid development of technology and technology has greatly benefited human life by expanding its range without time and space limitations, but at the same time, many negative effects and risks are increasing hour by minute and day by day. Fake news the process of intentionally or unintentionally spreading incorrect or false information online. Its main purpose is to change and confuse people's perception and opinion. Fake news is often activated during changes in international relations, geopolitical competition, internal and external social, political, and economic confrontations, public unrest, and any online. It is also the activity of broadcasting and distributing online news (content) that is purposefully misleading, with the aim of gaining financial and political benefits and advantages in traditional media, media and social networks.
DOI: 10.15468/dl.8myg57
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Ponera Testacea Emery, 1895 Stat. N. - A Sister Species Of P. Coarctata (Latreille, 1802) (Hymenoptera, Formicidae)",
 "Country is Croatia"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004031-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9sfj8v8jyc
title: the data source database

description: The county's carbon emissions from 2006 to 2017 were obtained from the CEDAs database，Calculation of carbon sink supported by land use data. The carbon emission data from 2017 to 2021 are obtained by fitting the nighttime light data with the carbon emission data.
DOI: 10.17632/9sfj8v8jyc.1
title: the data source database

description: The county's carbon emissions from 2006 to 2017 were obtained from the CEDAs database，Calculation of carbon sink supported by land use data. The carbon emission data from 2017 to 2021 are obtained by fitting the nighttime light data with the carbon emission data.
DOI: 10.17632/jpgggxz6xc
title: MA-LeafFruitDx: An Image Dataset for Deep Learning-Based Diagnosis of Mangrove Apple Diseases

description: The MA-LeafFruitDx (Sonneratia caseolaris) Leaf and Fruit Disease Classification Dataset is a curated image dataset designed to support advanced research in plant health monitoring using machine learning, deep learning, and computer vision. This dataset captures both healthy and diseased states of leaves and fruits of the Mangrove Apple (Sonneratia caseolaris), a plant native to the Sundarban mangrove forest in Bangladesh.

Total Number of Images; 
Original Dataset Size: 2,307 images
Augmented Dataset Size: 17,500 images (3,500 per class × 5 classes)

Class-wise Image Distribution (Original Dataset)
1. Healthy Fruits: 420
2. Healthy Leaves: 494
3. Insect Hole Leaves: 410
4. Unhealthy Fruits: 477
5. Yellow Leaves: 506

Data Augmentation: 
To address class imbalance and improve model generalization, the dataset was augmented to include 3,500 images per class, bringing the total dataset size to 17,500 images. The following augmentation techniques were applied:

- Geometric Transformations: Rotation, flipping (horizontal and vertical), random cropping
- Photometric Enhancements: Brightness adjustment, contrast modification, Gaussian blur
- Noise Injection: Gaussian noise and salt-and-pepper noise
- Color Space Variations: HSV shifts, grayscale conversion
- Perspective and Affine Transformations

Image Format: All images are in .jpg format.

Camera Devices Used: 
1. Samsung Galaxy S23+: 1,568
2. OnePlus GM1901: 739

Geographical Location: 
Data Collection Site: Sundarban Mangrove Forest, Bangladesh — a UNESCO World Heritage Site and home to a wide diversity of plant and animal species.

🚀 Application Areas
This dataset has diverse applications across ML, DL, and CV research fields:

✅ 1. Machine Learning
1. Feature engineering and selection for classical ML models (e.g., SVM, Random Forest, XGBoost)
2. Disease pattern classification using handcrafted features
3. Model benchmarking for low-resource environments

✅ 2. Deep Learning
1. Training and evaluation of Convolutional Neural Networks (CNNs)
2. Transfer learning with pre-trained models (e.g., ResNet, EfficientNet, InceptionV3)
3. Fine-grained image classification and object localization
4. Use in multi-class classification and semantic segmentation tasks

✅ 3. Computer Vision
1. Development of real-time plant disease detection systems
2. Implementation in mobile applications for on-field crop monitoring
3. Use in smart agriculture systems (IoT + CV)
4. Integration with image segmentation and disease severity scoring
5. Vision transformers (ViTs) and self-supervised learning exploration

This dataset provides a rich, high-quality resource for researchers aiming to advance the state-of-the-art in automated plant disease recognition, especially in mangrove ecosystems, which are often underrepresented in computer vision literature.
DOI: 10.17632/jpgggxz6xc.1
title: MA-LeafFruitDx: An Image Dataset for Deep Learning-Based Diagnosis of Mangrove Apple Diseases

description: The MA-LeafFruitDx (Sonneratia caseolaris) Leaf and Fruit Disease Classification Dataset is a curated image dataset designed to support advanced research in plant health monitoring using machine learning, deep learning, and computer vision. This dataset captures both healthy and diseased states of leaves and fruits of the Mangrove Apple (Sonneratia caseolaris), a plant native to the Sundarban mangrove forest in Bangladesh.

Total Number of Images; 
Original Dataset Size: 2,307 images
Augmented Dataset Size: 17,500 images (3,500 per class × 5 classes)

Class-wise Image Distribution (Original Dataset)
1. Healthy Fruits: 420
2. Healthy Leaves: 494
3. Insect Hole Leaves: 410
4. Unhealthy Fruits: 477
5. Yellow Leaves: 506

Data Augmentation: 
To address class imbalance and improve model generalization, the dataset was augmented to include 3,500 images per class, bringing the total dataset size to 17,500 images. The following augmentation techniques were applied:

- Geometric Transformations: Rotation, flipping (horizontal and vertical), random cropping
- Photometric Enhancements: Brightness adjustment, contrast modification, Gaussian blur
- Noise Injection: Gaussian noise and salt-and-pepper noise
- Color Space Variations: HSV shifts, grayscale conversion
- Perspective and Affine Transformations

Image Format: All images are in .jpg format.

Camera Devices Used: 
1. Samsung Galaxy S23+: 1,568
2. OnePlus GM1901: 739

Geographical Location: 
Data Collection Site: Sundarban Mangrove Forest, Bangladesh — a UNESCO World Heritage Site and home to a wide diversity of plant and animal species.

🚀 Application Areas
This dataset has diverse applications across ML, DL, and CV research fields:

✅ 1. Machine Learning
1. Feature engineering and selection for classical ML models (e.g., SVM, Random Forest, XGBoost)
2. Disease pattern classification using handcrafted features
3. Model benchmarking for low-resource environments

✅ 2. Deep Learning
1. Training and evaluation of Convolutional Neural Networks (CNNs)
2. Transfer learning with pre-trained models (e.g., ResNet, EfficientNet, InceptionV3)
3. Fine-grained image classification and object localization
4. Use in multi-class classification and semantic segmentation tasks

✅ 3. Computer Vision
1. Development of real-time plant disease detection systems
2. Implementation in mobile applications for on-field crop monitoring
3. Use in smart agriculture systems (IoT + CV)
4. Integration with image segmentation and disease severity scoring
5. Vision transformers (ViTs) and self-supervised learning exploration

This dataset provides a rich, high-quality resource for researchers aiming to advance the state-of-the-art in automated plant disease recognition, especially in mangrove ecosystems, which are often underrepresented in computer vision literature.
DOI: 10.24432/c5n015
title: SPECTF Heart
DOI: 10.17632/5pvcdrs5sc
title: Data for "The sound of silence: sex and individual differences in rat ultrasonic vocalisation"

description: Rat ultrasonic vocalisation data (calling rates and call type sequences) analysed regarding sex and individual differences in the paper "The sound of silence: sex and individual differences in rat ultrasonic vocalisation".
DOI: 10.17632/5pvcdrs5sc.1
title: Data for "The sound of silence: sex and individual differences in rat ultrasonic vocalisation"

description: Rat ultrasonic vocalisation data (calling rates and call type sequences) analysed regarding sex and individual differences in the paper "The sound of silence: sex and individual differences in rat ultrasonic vocalisation".
DOI: 10.17632/8g669bt2bt
title: AGU Tectonics Supporting Information for: Interaction between the Tethyan and Paleo-Pacific tectonic domains in Southeast Asia: Late Triassic initiation of an inward-dipping double subduction system

description: Contents of this file
1. Table S1. Detrital zircon U-Pb data for samples in this study.
2. Table S2. Detrital zircon Lu -Hf isotopic data for samples in this study.
3. Table S3. Compiled magmatic and detrital zircon U-Pb data.
4. Table S4. Compiled magmatic and detrital zircon Lu -Hf isotopic data.