cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.amte7x
title: Occurrence Download

description: A dataset containing 55698 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Oenanthe oenanthe (Linnaeus, 1758))",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 55698 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018057-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gwrr6wp57d
title: Global Consensus on the Management of Hyperpigmentation Disorders Part II: Table 5

description: Supplemental Table 5. "Hyperpigmentation of the folds: recommended treatment options and prevention measures.
DOI: 10.17632/gwrr6wp57d.1
title: Global Consensus on the Management of Hyperpigmentation Disorders Part II: Table 5

description: Supplemental Table 5. "Hyperpigmentation of the folds: recommended treatment options and prevention measures.
DOI: 10.7910/dvn/a31n0k
title: Executive Agreements Database, Statement Concerning the Investment Incentive Agreement with Maldives. Signed At Male March 15, 2010. Entered Into Force March 15, 2010

description: TIAS 10-315

First signed 03/15/2010
Last signed 03/15/2010
Entry into force (supplemented by last signed) 03/15/2010

CAR 2010-0081

stamped 2010-0081

C06544455

cover memo
DOI: 10.7910/dvn/a0zacg
title: Replication Data for: "Corrigendum to 'Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars'"

description: Corrigendum to "Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars". We present the results which correct the problems of the previously published models by producing more balanced classifications and also by achieving higher accuracy.
description: Programming language: Python
DOI: 10.7910/dvn/a7fxqu
title: Replication Data for: A Field Experiment in Motivating Employee Ideas

description: Gibbs, Michael, Neckermann, Susanne, and Siemroth, Christoph, (2017) "A Field Experiment in Motivating Employee Ideas." Review of Economics and Statistics 99:4, 577-590.
description: Michael
DOI: 10.15468/dl.j92q84
title: Occurrence Download

description: A dataset containing 438658 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "MediaType is Image",
 {
 "or" : [
 "ScientificName is Achillea millefolium L.",
 "ScientificName is Lotus corniculatus L.",
 "ScientificName is Trifolium pratense L."
 ]
 }
 ]
}

 The dataset includes 438658 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018038-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/o7bayf
title: Replication data for: "How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions"

description: Kasy, Maximilian, and Fessler, Pirmin, (2019) "How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions." Review of Economics and Statistics 101:4, 681-698.
DOI: 10.7910/dvn/a1uglt
title: A cruise-and-crash model of the cost of ruling

description: Since the publication of Nannestad and Paldam ([1999] on the cost of ruling, their finding that incumbents lose votes in the order of 2 to 3 percentage points per term has gained the status of something like an “inductive law” of politics (Budge [2019]. We suggest that this generalization conjures up an inaccurate image, that of a gradual reduction of a ruling party’s vote over the course of an incumbency or “spell.” Our own analyses of national and state or provincial elections in Australia, Canada, Germany, and the United States demonstrate that this interpretation is not correct. Instead, while they remain in office the ruling party cruises along, averaging more or less the same share of the vote election after election, until they fail to win another term, at which point all or nearly all of the cost of ruling, like a balloon payment on a loan, comes due. Moreover, on average the magnitude of the loss is independent of the length of their stay in office. This means that a long incumbency confounds the expectations fostered by the “inductive law” because the overall loss – which we find to be reasonably constant across a wide spectrum of data sets – divided by a spell length exponentially distributed from one to many terms cannot be a constant. Nannestad and Paldam's assertion of 2.25 percentage points per term vote loss is at best a description of a mid-range value. Our approach suggests that the cost of ruling is the product of two country-specific constants: an exponential decay of spell functions and an invariant percentage change in votes between the ousting election and the mean winning vote that is independent of spell duration. Thus, our method comes close to matching the inductive law at the mean, but also accounts for its variability along the number of terms in a spell. Moreover, standardizing the loss relative to the mean win vote yields a more general measure that cuts across countries and types of government. We estimate
that in most developed democracies the depth of the sink ranges between 15% and 25% below the mean win vote.
DOI: 10.17632/3xvk2f9wsk
title: Capítulo: La cooperación como estrategia de retroalimentación de la red de aprendizaje-servicio para la acción arquitectónica, urbano y territorial (RED-PASAUT 2023-24)

description: Este dataset contiene la referencia y acceso al capítulo "La cooperación como estrategia de retroalimentación de la red de aprendizaje-servicio para la acción arquitectónica, urbano y territorial (RED-PASAUT 2023-24)", publicado en Acciones Educativas Innovadoras en el ámbito universitario/ Dykinson (ISBN 979-13-7006-044-2) en 2025, y depositado en acceso abierto en el repositorio institucional IDUS de la Universidad de Sevilla.

En este trabajo se aborda [incluir un resumen de 3-4 líneas del contenido].

El capítulo forma parte de una línea de investigación orientada a la innovación docente en el ámbito de la arquitectura, el territorio y el Aprendizaje-Servicio Universitario. Puede consultarse públicamente en: 
🔗 https://hdl.handle.net/11441/170607
DOI: 10.17632/3xvk2f9wsk.1
title: Capítulo: La cooperación como estrategia de retroalimentación de la red de aprendizaje-servicio para la acción arquitectónica, urbano y territorial (RED-PASAUT 2023-24)

description: Este dataset contiene la referencia y acceso al capítulo "La cooperación como estrategia de retroalimentación de la red de aprendizaje-servicio para la acción arquitectónica, urbano y territorial (RED-PASAUT 2023-24)", publicado en Acciones Educativas Innovadoras en el ámbito universitario/ Dykinson (ISBN 979-13-7006-044-2) en 2025, y depositado en acceso abierto en el repositorio institucional IDUS de la Universidad de Sevilla.

En este trabajo se aborda [incluir un resumen de 3-4 líneas del contenido].

El capítulo forma parte de una línea de investigación orientada a la innovación docente en el ámbito de la arquitectura, el territorio y el Aprendizaje-Servicio Universitario. Puede consultarse públicamente en: 
🔗 https://hdl.handle.net/11441/170607
DOI: 10.17632/xhfjzt47j8
title: Data for An uncertainty-informed neural network-based (UINN) prognostic model for multi-type data

description: Case study data for "An uncertainty-informed neural network-based (UINN) prognostic model for multi-type data" submitted to IISE Transactions.
DOI: 10.17632/xhfjzt47j8.1
title: Data for An uncertainty-informed neural network-based (UINN) prognostic model for multi-type data

description: Case study data for "An uncertainty-informed neural network-based (UINN) prognostic model for multi-type data" submitted to IISE Transactions.
DOI: 10.35111/8gr4-tn81
title: Automatic Content Extraction for Portuguese

description: <h3>Introduction</h3>
<p>Automatic Content Extraction for Portuguese (LDC2024T05) was developed at <a href="https://www.inesctec.pt/en">INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci&ecirc;ncia</a> and consists of automatic Brazilian Portuguese and European Portuguese translations of the English text and annotations in <a href="../../../LDC2006T06">ACE 2005 Multilingual Training Corpus (LDC2006T06)</a>.</p>
<p>ACE 2005 Multilingual Training Corpus was developed by LDC to support the <a href="https://www.ldc.upenn.edu/collaborations/past-projects/ace">Automatic Contract Extraction (ACE)</a> program, specifically, by providing training data for the 2005 technology evaluation. It contains 1,800 files of mixed genre text in English, Arabic and Chinese annotated for entities, relations and events. The objective of the ACE program was to develop automatic content extraction technology to support automatic processing of human language in text form. Text genres included newswire, broadcast news, broadcast conversation, weblog, discussion forums, and conversational telephone speech.</p>
<h3>Data</h3>
<p>The English data was partitioned into training, development and test sets. The documents were split into sentences and each event mention was assigned to its sentence. Source sentences and their annotations were translated into Brazilian Portuguese using <a href="https://translate.google.com/">Google Translate</a> and into European Portuguese using <a href="https://www.deepl.com/en/translator">DeepL Translate</a>. An alignment algorithm and a parallel corpus word aligner were used to handle mismatches between translated annotations and their translated sentences.</p>
<p>Files are presented in JSON format.</p>
<h3>Samples</h3>
<p>Please view these samples (JSON): <a href="desc/addenda/LDC2024T05BR.json" target="_blank" rel="noopener">Portuguese (Brazil)</a> and <a href="desc/addenda/LDC2024T05PT.json" target="_blank" rel="noopener">Portuguese (Portugal)</a>.</p>
<h3>Updates</h3>
<p>None at this time.</p>
DOI: 10.7910/dvn/a1utyc
title: Replication Data for "Studying Nonobviousness"

description: This dataverse contains datasets and do files in Stata 16 format used in the article: 
Jason Rantanen, Lindsay Kriz, and Abigail Matthews. ​"Studying Nonobviousness." 73 Hastings Law Journal (2022).
description: Stata, 16
DOI: 10.17632/fh3ny8h4hz
title: Supplementary Data for Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A National User-Satisfaction Survey, UK

description: Supplementary Data for Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A National User-Satisfaction Survey, UK. Helen Couch , Andrew Pearson , Neha Malhotra , Michael Ferguson , George Couch , Clare Gilbert , Kate Morgan , Sarah Mann , Kelly Cassidy , Sarah Worthington , Elaine O'Shea , Maria SalomonEstebanez , Chris Worth , Senthil Senniappan , Indraneel Banerjee , Antonia Dastamani. The Journal of Clinical Endocrinology & Metabolism, dgaf313, https://doi.org/10.1210/clinem/dgaf313.

Includes all four surveys used in the CGM Surverys.pdf and the full figure of CGM-sat survey responses in Suppl Figure 1.tiff.
DOI: 10.17632/fh3ny8h4hz.1
title: Supplementary Data for Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A National User-Satisfaction Survey, UK

description: Supplementary Data for Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A National User-Satisfaction Survey, UK. Helen Couch , Andrew Pearson , Neha Malhotra , Michael Ferguson , George Couch , Clare Gilbert , Kate Morgan , Sarah Mann , Kelly Cassidy , Sarah Worthington , Elaine O'Shea , Maria SalomonEstebanez , Chris Worth , Senthil Senniappan , Indraneel Banerjee , Antonia Dastamani. The Journal of Clinical Endocrinology & Metabolism, dgaf313, https://doi.org/10.1210/clinem/dgaf313.

Includes all four surveys used in the CGM Surverys.pdf and the full figure of CGM-sat survey responses in Suppl Figure 1.tiff.
DOI: 10.7910/dvn/nzbjoc
title: Optical and Acoustic Breast Phantoms

description: This dataset includes a collection of numerical breast phantoms for use in various optical and acoustic imaging simulation studies including photoacoustic imaging, ultrasound computed tomography and diffuse optical tomography.&lt;br&gt;&lt;br&gt;
&lt;p&gt;
The numerical phantoms were generated from clinical Magnetic Resonance Angiography (MRA) data collected at the Washington University School of Medicine with IRB approval. The dataset comprises three sub-directories, each corresponding to a unique phantom representing one breast of a healthy female. Each file named "MergedPhantom.DAT" is a binary file of unsigned 8-bit integers that can be reshaped into a three-dimensional matrix where each voxel represents exactly one tissue type: background is 0, fibroglandular tissue is 2, fat is 3, skin is 4, and vessel is 5. Corresponding speed of sound, density, optical absorption, and optical scattering phantoms then can be easily created by assigning values to different tissue types within this phantom. The voxel size common to all the phantoms is 0.2 x 0.2 x 0.2 mm.&lt;br&gt;
&lt;p&gt;
Plaintext MATLAB scripts for loading the phantoms are given within each directory and the dimensions of each matrix of voxels may be read from those scripts.&lt;br&gt;
&lt;p&gt;
Phantom-specific parameters:
&lt;p&gt;
Neg_07_Left:&lt;br&gt; 
BREAST_TYPE = Scattered fibroglandular&lt;br&gt;
NX = 616, NY = 485, NZ = 719&lt;br&gt;

&lt;p&gt;
Neg_35_Left:&lt;br&gt;
BREAST_TYPE = Heterogeneously dense&lt;br&gt;
NX = 284, NY = 411, NZ = 722&lt;br&gt;

&lt;p&gt;
Neg_47_Left:&lt;br&gt;
BREAST_TYPE = Extremely dense&lt;br&gt;
NX = 495, NY = 615, NZ = 752&lt;br&gt;
description: Note that a few (around 7 out of 700) X-Y slices near the beginning and end of Z-range show some unnatural 90-degree lines. This is due to the limitation of our automatic algorithm to deal with some very low skin intensity in the original data. We recommend skipping these slices if you are conducting 2D simulations. If your 3D simulation is severely affected by such outlier slices, please let us know so we can work something out for you.
DOI: 10.17632/ypkkdd4nb4
title: Spandau et al. JAAD 2025 METHODS and CONSORT DIAGRAM

description: Supplemental Methods and CONSORT DIAGRAM for Spandau et al., 2025
DOI: 10.17632/ypkkdd4nb4.1
title: Spandau et al. JAAD 2025 METHODS and CONSORT DIAGRAM

description: Supplemental Methods and CONSORT DIAGRAM for Spandau et al., 2025
DOI: 10.17632/vj83jj6fvt.1
title: CT images and radiotherapy treatment planning of patients with breast cancer: A dataset

description: The data presented here were originally collected for the research project “Characterization of alpha wave frequency in psychophysiological insomnia and Non- psychophysiological insomnia subjects during sleep (Grant Number: XXX)”. Also, it reported in our study “Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients”. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. the lungs and heart objects were extracted from CT images along with compliance Dose plan. Dose-volume histogram (DVH) and Dose-mass histogram (DMH) extracted using CT images and dose plan matrix. Moreover, the complete clinical and dosimetric specifications of subjects is attached.
DOI: 10.17632/vj83jj6fvt.2
title: CT images and radiotherapy treatment planning of patients with breast cancer: A dataset

description: The data presented here were originally collected for the research project “Characterization of alpha wave frequency in psychophysiological insomnia and Non- psychophysiological insomnia subjects during sleep (Grant Number: XXX)”. Also, it reported in our study “Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients”. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. the lungs and heart objects were extracted from CT images along with compliance Dose plan. Dose-volume histogram (DVH) and Dose-mass histogram (DMH) extracted using CT images and dose plan matrix. Moreover, the complete clinical and dosimetric specifications of subjects is attached.
DOI: 10.17632/vj83jj6fvt
title: CT images and radiotherapy treatment planning of patients with breast cancer: A dataset

description: The data presented here were originally collected for the research project “Characterization of alpha wave frequency in psychophysiological insomnia and Non- psychophysiological insomnia subjects during sleep (Grant Number: XXX)”. Also, it reported in our study “Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients”. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. the lungs and heart objects were extracted from CT images along with compliance Dose plan. Dose-volume histogram (DVH) and Dose-mass histogram (DMH) extracted using CT images and dose plan matrix. Moreover, the complete clinical and dosimetric specifications of subjects is attached.
DOI: 10.17632/vj83jj6fvt.3
title: CT images and radiotherapy treatment planning of patients with breast cancer: A dataset

description: The data presented here were originally collected for the research project “Characterization of alpha wave frequency in psychophysiological insomnia and Non- psychophysiological insomnia subjects during sleep (Grant Number: XXX)”. Also, it reported in our study “Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients”. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. the lungs and heart objects were extracted from CT images along with compliance Dose plan. Dose-volume histogram (DVH) and Dose-mass histogram (DMH) extracted using CT images and dose plan matrix. Moreover, the complete clinical and dosimetric specifications of subjects is attached.
DOI: 10.17632/jttrv2w27r.1
title: Tomato Leaf Disease Classification Dataset

description: Dataset overview:
The dataset consists of 2995 high-resolution tomato leaf images, which are organized into seven different categories that include disease-stricken specimens, healthy leaf specimens, and deficiencies. These images were captured over different agricultural regions of Bangladesh during a time span of March 10 to March 20, 2025, while showing diverse environmental conditions as well as different leaf growth phases with various disease severity levels.
The classifications of the dataset consist of
Bacterial Spot—117 images
Yellow Leaf Curl Virus—234 images
Early Blight—524 images
Healthy—380 images
Late Blight—112 images
Leaf Miner Flies—884 images
Magnesium Deficiency—744 images
Purpose:
The data provides necessary elements to establish automatic disease identification systems on tomato plants through image processing tools alongside machine learning techniques. The system possesses functions that can develop AI models for precise agriculture and educate deep learning systems for implementing sustainable farming methods. The data set allows three fundamental applications, which include supervised learning model design as well as feature extraction analysis and real-time disease detection systems implementation.
DOI: 10.7910/dvn/a63pzx
title: Replication Materials for: Estimating Endogenous Effects on Ordinal Outcomes

description: Review of Economics and Statistics: Forthcoming
description: Volume XX, Issue X
DOI: 10.17632/jttrv2w27r
title: Tomato Leaf Disease Classification Dataset

description: Dataset overview:
The dataset consists of 2995 high-resolution tomato leaf images, which are organized into seven different categories that include disease-stricken specimens, healthy leaf specimens, and deficiencies. These images were captured over different agricultural regions of Bangladesh during a time span of March 10 to March 20, 2025, while showing diverse environmental conditions as well as different leaf growth phases with various disease severity levels.
The classifications of the dataset consist of
Bacterial Spot—117 images
Yellow Leaf Curl Virus—234 images
Early Blight—524 images
Healthy—380 images
Late Blight—112 images
Leaf Miner Flies—884 images
Magnesium Deficiency—744 images
Purpose:
The data provides necessary elements to establish automatic disease identification systems on tomato plants through image processing tools alongside machine learning techniques. The system possesses functions that can develop AI models for precise agriculture and educate deep learning systems for implementing sustainable farming methods. The data set allows three fundamental applications, which include supervised learning model design as well as feature extraction analysis and real-time disease detection systems implementation.
DOI: 10.17632/jttrv2w27r.2
title: Tomato Leaf Disease Classification Dataset

description: Dataset overview:
The dataset consists of 2995 high-resolution tomato leaf images, which are organized into seven different categories that include disease-stricken specimens, healthy leaf specimens, and deficiencies. These images were captured over different agricultural regions of Bangladesh during a time span of March 10 to March 20, 2025, while showing diverse environmental conditions as well as different leaf growth phases with various disease severity levels.
The classifications of the dataset consist of
Bacterial Spot—117 images
Yellow Leaf Curl Virus—234 images
Early Blight—524 images
Healthy—380 images
Late Blight—112 images
Leaf Miner Flies—884 images
Magnesium Deficiency—744 images
Purpose:
The data provides necessary elements to establish automatic disease identification systems on tomato plants through image processing tools alongside machine learning techniques. The system possesses functions that can develop AI models for precise agriculture and educate deep learning systems for implementing sustainable farming methods. The data set allows three fundamental applications, which include supervised learning model design as well as feature extraction analysis and real-time disease detection systems implementation.
DOI: 10.15468/dl.qj6qud
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Bauhinia orbiculata A.C.B.Santos & L.P.Queiroz"
 ]
}

 The dataset includes 53 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018073-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.