cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/vh5soq
title: Passed Acts; St. 1843, c.69, SC1/series 229

description: &lt;p&gt;Petition subject: Great Massachusetts Petition &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:13448109"&gt;http://nrs.harvard.edu/urn-3:FHCL:13448109&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Walpole &lt;/p&gt; &lt;p&gt;Total signatures: 133 &lt;/p&gt; &lt;p&gt;Unidentified signatures: 133 &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: additional documents available &lt;/p&gt; &lt;p&gt;Additional archivist notes: George Latimer [note: this data reflects the information on the spreadsheet submitted by Henry I. Bowditch and William F. Channing to the legislature, which was filed with St. 1843, c.69, passed April 26, 1843. The majority of the Great Massachusetts Petition has not been located.] &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: St. 1843, c.69, passed April 26, 1843 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;
DOI: 10.7910/dvn/26324
title: Technology, Income Distribution and Poverty in the Philippines (1997)

description: The study is based on village full enumeration field surveys. The main purpose of the study is to examine the changes in the structure of rural household income and its distribution over the past decade with primary data generated through in-depth household surveys in four villages with contrasting production environments. This study also shed light on the changes in the poverty situation of rural households over the 1985-97 period.
description: Subject: Number of Repondents Type: Sample Size Notes: 252;
DOI: 10.7910/dvn/hq6vzt
title: Registrations in the AEA RCT Registry (2013-04 to 2020-09)

description: This dataset contains publicly available data on the American Economic Association's RCT registry downloaded September 4, 2020.
DOI: 10.7910/dvn/hs5r5s
title: Replication Data for: What Politicians Do Not Know Can Hurt You: The Effects of Information on Politicians' Spending Decisions

description: Replication and supplementary files for "What politicians don't know can hurt you: The effects of information on politicians' spending decisions"
DOI: 10.7910/dvn/jwzjfo
title: Replication Data for: 'National Games for Local Gains: Legislative Activity, Party Organization and Candidate Selection’, Journal of Elections, Public Opinion and Parties, 2018 (M. Chiru, S. Gherghina)

description: This dataset includes the replication files for the article: Chiru, M. & Gherghina, S. 2018. 
 'National Games for Local Gains: Legislative Activity, Party Organization and Candidate Selection’, Journal of Elections, Public Opinion and Parties.
DOI: 10.7910/dvn/26295
title: Technology, Income Distribution and Poverty in the Philippines (1996)

description: The study is based on village full enumeration field surveys. The main purpose of the study is to examine the changes in the structure of rural household income and its distribution over the past decade with primary data generated through in-depth household surveys in four villages with contrasting production environments. This study also shed light on the changes in the poverty situation of rural households over the 1985-97 period.
description: Subject: Number of Repondents Type: Sample Size Notes: 915;
DOI: 10.7910/dvn/26467
title: Replication data for: Connecting the Candidates: Consultant Networks and the Diffusion of Campaign Strategy in American Congressional Elections

description: Modern American political campaigns are typically conceptualized as "candidate-centered" and treated as conditionally independent in quantitative analyses. In reality, however, these campaigns are linked by professional consulting firms, who are important agents of campaign strategy diffusion within the extended party networks of the contemporary era. To test our hypothesis that consultants disseminate campaign strategies among their clients, we analyze new data on U.S. House elections derived from Federal Election Commission records. Using spatial autoregressive models, we find that candidates who share consultants are more likely to use similar campaign strategies than we would otherwise expect conditional on numerous explanatory variables. These results, which largely withstand an extensive series of robustness and falsification tests, suggest that consultants play a key role in diffusing strategies among Congressional campaigns.
DOI: 10.7910/dvn/26530
title: Replication data for: Government-Opposition or Left-Right? The Institutional Determinants of Voting in Legislatures

description: We use roll-call voting data from 16 legislatures to investigate how the institutional context of politics Ã¢ÂÂ such as whether a country is a parliamentary or presidential regime, or has a single-party, coalition or minority government Ã¢ÂÂ shapes coalition formation and voting behaviour in parliaments. We use a geometric scaling metric to estimate the Ã¢ÂÂrevealed spaceÃ¢ÂÂ in each of these legislatures and a vote-by-vote statistical analysis to identify how much of this space can be explained by government-opposition dynamics as opposed to (left-right) policy positions of parties. We find that government-opposition interests rather than partiesÃ¢ÂÂ policy positions are the main drivers of voting behaviour in most institutional contexts. In contrast, we find that issue-by-issue coalition-building along a single policy dimension only exists under restrictive institutional constraints; namely presidential regimes with coalition governments or parliamentary systems with minority governments. Put another way, voting in most legislatives is more like Westminster than Washington, DC.
DOI: 10.7910/dvn/26546
title: Replication data for: From Posting to Voting: The Effects of Political Competition on Online Political Engagement

description: copy directly from abstract in PSRM publication
DOI: 10.15468/dl.a3rk77
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina Linnaeus, 1758",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((42.8012 -16.5547,42.4342 -16.6376,42.2188 -17.038,42.391 -17.4288,42.6657 -17.5533,43.0375 -17.4752,43.2579 -17.0968,43.0959 -16.69,42.8012 -16.5547))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012836-250525065834625/datasets/export for details.
DOI: 10.15468/dl.bbtzqr
title: Occurrence Download

description: A dataset containing 38106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Melica nutans L.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 38106 records from 179 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012837-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fautfn
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina Linnaeus, 1758",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((40.3006 -16.7147,40.6365 -16.1504,35.9648 -16.1504,35.9648 -19.5556,36.0796 -19.4498,36.663 -19.2358,37.5966 -18.1093,38.5624 -17.638,39.3949 -17.5133,39.577 -17.186,40.3006 -16.7147))"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012838-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ke7yrc
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Melica subulata (Griseb.) Scribn.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 260 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012839-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gb57s7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina Linnaeus, 1758",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.9919 -21.4718,35.9648 -21.4413,35.9648 -24.1383,36.0558 -23.8719,35.9919 -21.4718))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012840-250525065834625/datasets/export for details.
DOI: 10.15468/dl.sjek35
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Melocactus bahiensis (Britton & Rose) Luetzelb.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 32 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012841-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7937/29kw-ae92
title: Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images
title: CDD-CESM

description: Deep learning (DL) has a promising potential in reducing the workload of radiologists and helping them provide a more accurate diagnosis. However, fully annotated and large-sized datasets are required. This dataset is a collection of 2,006 high-resolution Contrast-enhanced spectral mammography (CESM) images with annotations and medical reports. 
Acquisition protocol: 

CESM is done using the standard digital mammography equipment, with additional software that performs dual-energy image acquisition. Two minutes after intravenously injecting the patient with non-ionic low-osmolar iodinated contrast material (dose: 1.5 mL/kg), craniocaudal (CC) and mediolateral oblique (MLO) views are obtained. Each view comprises two exposures, one with low energy (peak kilo-voltage values ranging from 26 to 31kVp) and one with high energy (45 to 49 kVp). Low and high-energy images are then recombined and subtracted through appropriate image processing to suppress the background breast parenchyma. A complete examination is carried out in about 5-6 minutes.

Image preprocessing:

The images were converted from DICOM to JPEG using RadiAnt with best 100% image quality (lossless).  They have an average of 2355 x 1315 pixels.

Supporting data:

Full medical reports are also provided for each case (DOCX) along with manual segmentation annotation for the abnormal findings in each image (CSV file).  Each image with its corresponding manual annotation (breast composition, mass shape, mass margin, mass density, architectural distortion, asymmetries, calcification type, calcification distribution, mass enhancement pattern, non-mass enhancement pattern, non-mass enhancement distribution, and overall BIRADS assessment) is compiled into 1 Excel file. 

https://www.robots.ox.ac.uk/~vgg/software/via/via.html was used for the segmentation annotation.  It can be used to show the annotations on the images by clicking on Annotation--> import annotations (from csv), and then proceeding to upload any image to view the annotations drawn over it.  Moreover, a helper repository is created to help with pre-processing, model training, model evaluation, and segmentation annotation loading: https://github.com/omar-mohamed/CDD-CESM-Dataset

Regarding the tabs on the annotations Excel file, these are commonly used radiological descriptors as defined by the American College of Radiology 2013 lexicon.
DOI: 10.7937/44ma-gx21
title: 3D pathology of prostate biopsies with biochemical recurrence outcomes: raw H&E-analog datasets and image translation-assisted segmentation in 3D (ITAS3D) datasets (PCa_Bx_3Dpathology)

description: This collection provides public access to a 3D pathology dataset of prostate cancer, allowing researchers to further investigate various 3D tissue structures and their correlation with prostate cancer patient outcomes (biochemical recurrence).   These 3D tissue structures are revealed through: (1) a H&E-analog stain, (2) synthetically generated immunofluorescence staining of CK8 (targeting the luminal epithelial cells of all prostate glands), and (3) 3D segmentation masks of the gland lumen, epithelium, and stromal regions of prostate biopsies.  This data collection will promote research in the field of computational 3D pathology for clinical decision support.
In this TCIA collection, we provide the raw 3D open-top light-sheet (OTLS) microscope images of prostate biopsies stained with a fluorescent analog of H&E at original resolution (unfused image tiles), the 2x down-sampled fused OTLS-imaged images (H&E-analog staining), the synthetic cytokeratin-8 (CK8) immunofluorescent images at 2x-downsampled resolution, the 3D semantic segmentation masks of glands at 4x down-sampled resolution, the clinical data for patient outcomes (biochemical recurrence), and the coordinates for the cancer-enriched regions of each biopsy. All datasets are from the 50 patient cases studied in this publication: [W. Xie et al., Cancer Research, 2022].
Note that the 3D pathology datasets provided in this collection were generated in Dr. Jonathan Liu’s lab at the University of Washington with a custom open-top light-sheet (OTLS) microscope developed by the lab [A.K. Glaser et al., Nature Communications, 2019].  There is no clinical metadata within the imaging files and all patients are referred to with coded identifiers.  All of the clinical outcomes data provided in this collection have already been published within the supplement of [W. Xie et al., Cancer Research, 2022].
DOI: 10.7937/4qad-4280
title: Annotations for Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma (AHOD0831-Tumor-Annotations)
title: AHOD0831-Tumor-Annotations

description: This dataset contains image annotations derived from the NCI Clinical Trial "Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma (AHOD0831)”.  The key objective of this project is to generate a large and highly curated imaging dataset of pediatric Hodgkin lymphoma patients with annotations suitable for cancer researchers and AI developers.

Annotation Protocol
For each patient, every DICOM Study and DICOM Series was reviewed to identify and annotate the clinically relevant time points and sequences/series.  Segmentations, Seed Points, and Negative Findings Assessments are provided.
DOI: 10.7937/91ah-v663
title: Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Radiology Database (RICORD) Release 1c - Chest x-ray, Covid+ (MIDRC-RICORD-1C)

description: The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models. Purpose: To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.
DOI: 10.7937/c7x1-yn57
title: Standard and Delayed Contrast-Enhanced MRI of Malignant and Benign Breast Lesions with Histological and Clinical Supporting Data (Advanced-MRI-Breast-Lesions)

description: The application of deep-learning methods to breast MRI has a potential to improve diagnosis as well as predict pathological features of the tumors and their response to therapy, and is currently widely investigated. Standard contrast-enhanced MRI of the breast provides high sensitivity but variable specificity in detecting breast cancer, and may lead to excessive benign biopsies. Large, well annotated datasets are needed to improve the current results in this field.
This dataset is a single-institutional, retrospective collection of 632 breast-MRI imaging sessions acquired on a 1.5T MR system between 2018-2021. Supporting data, collected for 200 patients from clinical notes, radiology report and pathology reports, is included: patient age, lesion location, malignant/benign outcome, pathology and grade (if applicable), receptor status and KI67 (if applicable). The main uniqueness of this data is that it contains the standard protocol series (T1-weighted DCE sequence with 5 time points- before and 4 time points after contrast injection, and T2-weighted MRI with/without fat suppression) as well as the delayed contrast T1-images acquired 20-28 minutes after contrast administration and the calculated treatment response assessment maps (TRAMs).
DOI: 10.7937/jc8x-9874
title: RSNA-ASNR-MICCAI-BraTS-2021

description: This dataset includes brain MRI scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., T1, T1c, T2, T2-FLAIR) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their MGMT promoter methylation status. These scans are a collection of data from existing TCIA collections, but also cases provided by individual institutions and willing to share with a cc-by license. The BraTS dataset describes a retrospective collection of brain tumor structural mpMRI scans of 2,040 patients, acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. The 4 structural mpMRI scans included in the BraTS challenge describe a) native (T1) and b) post-contrast T1-weighted (T1Gd (Gadolinium)), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, acquired with different protocols and various scanners from multiple institutions. Furthermore, data on the O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is provided as a binary label. Notably, MGMT is a DNA repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response. It is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status.
DOI: 10.7937/m0pb-bh69
title: A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization (CT4Harmonization-Multicentric)

description: This collection introduces an open-source, anthropomorphic phantom-based dataset of CT scans for developing harmonization methods for deep learning based models. The phantom mimics human anatomy, allowing repeated scans without radiation delivery to real patients and isolating scanner effects by removing inter- and intra-patient variations. The dataset includes 268 image series from 13 scanners, 4 manufacturers, and 8 institutions, repeated 18-30 times at a 10 mGy dose using a harmonized protocol. An additional 1,378 image series were acquired with the same 13 scanners and  harmonized protocol but including additional acquisition doses. The presented phantom scans consist of three compartments from thorax, liver and test patterns. The 3D-printed liver includes three types of abnormal regions of interest, including two cysts, a metastasis, and a hemangioma, with ground truth segmentation masks that could be used for classification and segmentation.
DOI: 10.7937/qvaz-ja09
title: Mediastinal Lymph Node Quantification (LNQ): Segmentation of Heterogeneous CT Data
title: Mediastinal-Lymph-Node-SEG

description: Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Machine learning models for medical image segmentation have shown remarkable progress in recent years, but we know of no tools available to fully quantify lymph nodes. Lymph nodes are often within the intensity profile of normal soft tissue and have ill-defined borders. Additionally, their presentation across subjects can vary significantly, making it difficult to scale from small datasets to a robust tool.

 This dataset contains chest CT scans from 540 patients acquired during treatment for various cancer types, curated for the LNQ2023 MICCAI Challenge (https://lnq2023.grand-challenge.org/ ) to help develop new segmentation tools from weakly annotated cases. The dataset contains both partially annotated (i.e., one mediastinal lymph node out of several in the case are segmented) as well as fully annotated (i.e., all mediastinal lymph nodes are segmented) cases and clinical data (gender/cancer type).