The Connecticut Crash Data Repository (CTCDR) is a web tool designed to provide access to select crash information collected by state and local police. This data repository enables users to query, analyze and print/export the data for research and informational purposes. The CTCDR is comprised of crash data from two separate sources; The Department of Public Safety (DPS) and The Connecticut Department of Transportation (CTDOT). The purpose of the CTCDR is to provide members of the traffic-safety community with timely, accurate, complete and uniform crash data. The CTCDR allows for complex queries of both datasets such as, by date, route, route class, collision type, injury severity, etc. For further analysis, this data can be summarized by user-defined categories to help identify trends or patterns in the crash data.

EdSight is an education data portal that integrates information from over 30 different sources – some reported by districts and others from external sources. The portal can be accessed here: http://edsight.ct.gov/. Information is available on key performance measures that make up the Next Generation Accountability System, as well as dozens of other topics, including school finance, special education, staffing levels and school enrollment.

The State Data Plan is required to be developed in accordance with Public Act 18-175 . Specifically the Act requires that the state data plan shall: Purpose of the plan: establish management and data analysis standards across all executive branch agencies, include specific, achievable goals within the two years following adoption of such plan, as well as longer term goals, make recommendations to enhance standardization and integration of data systems and data management practices across all executive branch agencies, provide a timeline for a review of any state or federal legal concerns or other obstacles to the internal sharing of data among agencies, including security and privacy concerns, and set goals for improving the open data repository. An initial draft of the plan is due on or before November 1, 2018 with a final plan due December 31, 2018. The plan is required to be updated every two years thereafter. Once final, information technology actions of affected state agencies are required to be consistent with the plan. Further, the Chief Data Officer is required to establish procedures for each affected agency to reports on the agency’s progress toward achieving the goals articulated in the plan. Feedback on the Phase 1 Draft is welcome and will be accepted until August 24. About the Process The plan will be developed iteratively, in phases, each with an opportunity for both public and agency input. Phase 1: Release and feedback on a broad set of principles and goals to guide the development of the plan and establish a vision for improving the management, use, and sharing of data for state agencies. Additionally, this phase includes several “Focal Points” or areas where the state should focus its efforts for the initial 2 year period that the plan will cover. Phase 2: Finalize state data plan principles and add more specific actions under each principle that will assist in guiding agency actions. Finalize the overarching goals and supplement with more specific measurable objectives that will advance each goal. Finalize “Focal Points” and supplement with additional steps to help advance implementation. Collect feedback from both the public and state agencies. Phase 3: Present a draft of the state data plan for review and feedback from the Data Analysis Technology Advisory board established pursuant to Public Act 18-175

This dataset tracks the updates made on the dataset "COVID-19 State Profile Report - Connecticut" as a repository for previous versions of the data and metadata.

Connecticut Shapefile and Voter Registration

Comprehensive dataset of 9 Store equipment suppliers in Connecticut, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This repository contains all the materials necessary for training deep-learning based algorithms
for the DL-spectral CT Challenge.
The training data folder contains 1000 cases with which to train your networks.
For more information about the spectral CT modeling, please see starting_kit.tgz.

Data are in python numpy's .npy format, and arrays are stored in float32 (single precision).
The .npy files can be read into python with the numpy.load command, yielding single precision
floating point arrays of the proper dimensions.

Files:
Phantom_[Tissue].npy.gz ([Tissue] can be Adipose, Fibroglandular, or Calcification)
These arrays are 1000x512x512. Files are gzipped for faster data transfer.
1000 images of pixel dimensions 512x512.
These are ground truth Tissue maps for the simulated breast phantom.

[low/high]kVpTransmission.npy.gz
These arrays are 1000x256x1024
The represent 1000 cases of the low and high kVp transmission data, each of which has 256 projections
onto a linear 1024-pixel detector.

[low/high]kVpImages.npy.gz
These arrays are 1000x512x512
1000 images of pixel dimensions 512x512, generated from [low/high]kVpTransmission.npy
by standard negative logarithm processing followed by filtered back-projection (FBP)
image reconstruction.

For the challenge, the goal will be to estimate (or predict) Phantom_[Tissue].npy
from either [low/high]kVpTransmission.npy or [low/high]kVpImages.npy or both.

Iterative or model-based approaches will necessarily use [low/high]kVpTransmission.npy as input.
Deep-learning approaches can be:
solely image-to-image; i.e. [low/high]kVpImages -> Phantom[Tissue] prediction.
solely data-to-image; i.e. [low/high]kVpTransmission -> Phantom[Tissue] prediction.
Or some combination of the two.
Hybrid iterative/deep-learning approaches are also acceptable.

Participants using image-to-image approaches do not need to know the spectral CT model.
All other approaches will need this knowledge and model specifications are
provided in the folder, which has been packed in the gzipped tar-file starting_kit.tgz.

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning spectral CT Grand Challenge. Med Phys. 2024; 51: 772–785.

See link below in Related Works.

Graph and download economic data for Monthly State Retail Sales: Miscellaneous Store Retailers in Connecticut (MSRSCT453) from Jan 2019 to Apr 2025 about miscellaneous, CT, retail trade, sales, retail, and USA.

Included here is a geothermal industry workforce needs assessment report for Connecticut. As part of the DOE-funded Community Geothermal Heating and Cooling Design and Deployment grant, Northeast Energy Efficiency Partnership (NEEP) conducted several online surveys to gain a better understanding of the current geothermal workforce in Connecticut as well as gaps and needs that can be addressed to better support the geothermal workforce. The surveys also looked into training opportunities for geothermal in Connecticut. Attached here is the needs assessment report that was produced from the surveys. The report's appendix includes all survey results from facility managers, training centers, as well as drillers, installers, manufacturers, and engineers. The names of respondents have been redacted.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Repository. The data include parameters of repository with a geographic location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Monthly State Retail Sales: Miscellaneous Store Retailers in Connecticut was 7.90000 % Chg. from Yr. Ago in February of 2025, according to the United States Federal Reserve. Historically, Monthly State Retail Sales: Miscellaneous Store Retailers in Connecticut reached a record high of 77.90000 in April of 2021 and a record low of -32.40000 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Monthly State Retail Sales: Miscellaneous Store Retailers in Connecticut - last updated from the United States Federal Reserve on July of 2025.

As part of efforts to expand the Local Geohistory Project, which aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government, this repository has been created to disseminate law index data for the State of Connecticut. This index currently covers private and special laws only from 1789 through 1943.

Private and special laws concern one or several individuals, entities, or localities, unlike public or general laws, which apply to all similarly situated individuals, entities, and localities within a jurisdiction. Throughout New England, private and special laws were often the primary method used to alter municipal and county boundaries and forms of government.

The law index is released as a tab-separated values (TSV) file, output/ConnLawIndex.tsv. The Detail column may contain additional Prefix information that is repeated from prior entries.

This repository does not contain the full text of the laws, nor does it currently contain links to the full text. Because the index was created using OCR technology, it may contain uncaptured errors.

Comprehensive dataset of 339 Medical supply stores in Connecticut, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The dataset used in our article was uploaded on our Google Drive account and the download link is provided as follows:https://drive.google.com/drive/folders/1-tXCaPtv0vupjXeLvrEX2cGt3tCOqyiv?usp=sharingSix compressed files (named as clinical.zip, COVID-19.zip, CXR.zip, Influenza.zip, Normal.zip and Pneumonia.zip) were included in the download address with a total size of 31.7GB.The sample of this is study is a multi-modal dataset consisting of X-ray image data (X-data), CT image data (CT-data) and clinical indicators data (Clinical-data).The X-data of COVID-19 cases collected from the CCD contained 212 patients diagnosed with COVID-19. We also collected 5,100 normal cases and 3,100 pneumonia cases from the RSNA. In addition, The X-data collected from the Youan hospital contained 45 cases diagnosed with COVID-19, 503 normal cases, 435 cases diagnosed with pneumonia(not COVID-19 patients), and 145 cases diagnosed as influenza.We collected CT-data of 120 normal cases from the LUNA-16. We also collected the CT-data of 215 pneumonia cases from the ICNP. The CT-data collected from the Youan hospital contained 95 patients diagnosed with COVID-19, 50 patients diagnosed with influenza and 215 patients diagnosed with pneumonia.The Clinical-data contained 95 clinical indicators data pairs of COVID-19 (369 images of the lesion area and 95×5 clinical indicators).The persistent web links for the four public data repositories are listed as below:

CCD: (https://github.com/ieee8023/covid-chestxray-dataset)

RSNA: (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge)

LUNA16: (https://luna16.grand-challenge.org/Data/)

ICNP: (https://data.mendeley.com/datasets/kk6y7nnbfs/1)

Package Store Permit Availability by town. Updated nightly.

In a submitted manuscript, test an image capture method using smartphone camera video-derived images of brain computed tomography (CT) scans of traumatic intracranial hemorrhage. The deidentified videos are emailed or uploaded from the emergency department for central adjudication.

We measured the time in seconds it took to capture and send the files. The primary outcomes were hematoma volume measured by ABC/2, Marshall Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture. A radiologist and an imaging scientist applied ABC/2 method, calculated the Marshall scale and midline shift on the data acquired on different smartphones and the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.

Background

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. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. 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.

Materials and Methods

This dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.

Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.

Data Abstract

1. 120 de-identified Thoracic CT scans from COVID negative patients.

2. Supporting clinical variables: MRN*, Age, Exam Date/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

This dataset tracks the updates made on the dataset "SNOMED CT" as a repository for previous versions of the data and metadata.

Computed tomography (CT) enables rapid imaging of large-scale studies of bone, but those datasets typically require manual segmentation, which is time-consuming and prone to error. Convolutional neural networks (CNNs) offer an automated solution, achieving superior performance on image data. Here, we used CNNs to train segmentation models from scratch on 2D and 3D patches from micro-CT scans of otter long bones. These new models, collectively called BONe (Bone One-shot Network), aimed to be fast and accurate, and we expected enhanced results from 3D training due to better spatial context. Our results showed that 3D training required substantially more memory. Contrary to expectations, 2D models performed slightly better than 3D models in labeling details such as thin trabecular bone. Although lacking in some detail, 3D models appeared to generalize better and predict smoother internal surfaces than 2D models. However, the massive computational c..., Materials Limb bones from the North American river otter (Lontra canadensis) were borrowed from four museums — OMNH (SNM): Sam Noble Oklahoma Museum of Natural History (Norman Oklahoma); UAM: University of Alaska Museum of the North (Fairbanks, Alaska); and UF: Florida Museum of Natural History (Gainesville, Florida); UWBM (BM): Burke Museum of Natural History and Culture (Seattle, Washington). In total, the sample consisted of 38 elements (humerus, radius, ulna, femur, tibia, and fibula) from nine individuals.

Specimen

kV

µA

Filter

Res. (µm)

Provenance

Sex

Side

Element

Group

OMNH 44262

160

312

Copper

49.99

Tennessee

F

R

Humerus

Fitting

L

Radius

Fitting

L

Ulna

Fitting

OMNH 53994

160

312

Copper

49.99

Tennessee

M

L

Femur

Fitting

&..., , # Dataset: Segmentation of cortical bone, trabecular bone, and medullary pores from micro-CT images using 2D and 3D deep learning models

https://doi.org/10.5061/dryad.b2rbnzsq4

Introduction: The following document outlines the structure of data repository.

“_composite.zip†contains separate folders for the raw micro-CT slices and CTP labels used for model fitting. Because Avizo’s deep learning segmentation module can only load a single pair of data files (i.e., one file consists of the raw slices and the second file consists of the labels), we appended the samples used for fitting into a composite sample.

“_Models.zip†contains the deep learning models for bone-pore and cortical-trabecular-pores segmentation. Model files are intended for use in the commercial software Avizo/Amira but are compatible with a variety of both open source and commercial software (e.g., TensorFlow or Comet Dragonfly). Files consist of model weights (HDF5 format...

X-ray CT scan carbG_C_A of carbonado diamond (23.45 carat, Central African Republic) for Richard Ketcham of UT with Christian Koeberl of the University of Vienna. Specimen scanned by Richard Ketcham 18 September 2010. Voxel size (XYZ) =2.06 µm. Total slices = 985. Please acknowledge the University of Texas High-Resolution X-ray CT Facility (UTCT), Richard Ketcham, and Christian Koeberl when using these data. This research was supported in part by National Science Foundation grant EAR-0948842.