Lunar regolith simulants are manufactured in order to provide a higher volume, much less expensive and more available source of material, compared to real lunar regolith material, upon which to test various instruments and machines that are being designed to operate on the lunar surface. There are many sources of these materials. However, the three-dimensional (3D) shape of these materials has never been characterized and used to compare to each other and to real lunar regolith material brought back from the Apollo missions. The focus of this database is to provide 3D shape and size information for each of 17 lunar regolith materials (8 mare, 9 highland). Over 1.1 million particles are in this database, with their 3D shape stored as STL files. Geometric information about each particle is in the database, as well as the original X-ray CT images from which the particles were extracted.

Reupload of anonymized postmortem CT scans of the whole body originally published by Michael Kistler through the SICAS Medical Image Repository (smir.ch) as open access Virtual Skeleton Database (VSD). The CT datasets were provided by the forensic institutes of the universities of Bern and Zürich and shared under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license after ethical approval of the Cantonal Ethics Committee Bern. Further information can be found in:

• Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R. & Büchler, P. The virtual skeleton database: an open access repository for biomedical research and collaboration. Journal of Medical Internet Research 15(11), e245; 10.2196/jmir.2930 (2013).
• Kistler, M. A database framework to incorporate statistical variability in biomechanical simulations. PhD thesis. Faculty of Medicine of the University of Bern; boristheses.unibe.ch/916 (2014).

Due to ongoing difficulties in accessing the SMIR website a mirror of the original VSDFullBody datasets without any alterations is provided.

CAUTION: The VSD contains a few inconsistencies, such as duplicate CT datasets. The uploader is not connected to the SMIR or VSD and, therefore, not responsible for errors in the VSD. However, errors that the uploader recognized during the work with the VSD were logged in an Excel file: VSD_Comments.xlsx

Datasets of the VSD were used for the creation of surface models of the lower body's osseous anatomy. Further information can be found in:

• Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 10, 763; 10.1038/s41597-023-02669-z (2023).

This study describes a subset of the HNSCC collection on TCIA.

PURPOSE:

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Publicly shared RT data is scarce due to high complexity of RT structure data and the need for registration in time, space, and across planning sets. We here introduce an open access imaging database for patients treated with radiotherapy for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS:

2840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013 were screened. Patients with whole-body PET-CT or abdominal CT scans both before and after RT were included (n=215). Clinical data were retrieved from the MD Anderson Cancer Center custom electronic medical record system, ClinicStation. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. All files were de-identified and transferred to The Cancer Imaging Archive servers using the RSNA Clinical Trial Processor program. Files were screened for errors or residual PHI using TagSniffer and Posda Tools software, reviewed by TCIA curators, then confirmed at the parent institution.

RESULTS:

The HNSCC collection is a dataset consisting of 433,384 DICOM files from 3,225 series and 765 studies collected from 215 patients, which includes de-identified diagnostic imaging, radiation treatment planning, and follow up imaging. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.

CONCLUSION:

Recent advances in data archiving, patient de-identification, and image registration have allowed for the creation of a high quality RT-enriched imaging database within TCIA. Open access to these data allows for interinstitutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing. A related dataset describing the other component of the HNSCC collection is here: Radiomics outcome prediction in Oropharyngeal cancer DOI: 10.7937/TCIA.2020.2vx6-fy46

X-ray micro-tomography (micro-CT) is a non-destructive imaging technique that provides resolution of material microstructures in three dimensions at scales from hundreds of nanometers to centimeters. The technique is used at NASA Ames Research Center to investigate a variety of advanced materials, including lightweight composite heatshields for atmospheric entry, woven materials, parachute textiles and meteoroids. Data are collected using both synchrotron and laboratory-based X-ray sources. Synchrotron micro-CT is performed in collaboration with the beamline 8.3.2 at the Advanced Light Source at Lawrence Berkeley National Laboratory. High-fidelity digital representations of microstructures obtained from micro-CT are used as framework to perform predictive simulations of effective material properties and material response using high performance computing.

Context

Zhao, B., James, L. P., Moskowitz, C. S., Guo, P., Ginsberg, M. S., Lefkowitz, R. A., … Schwartz, L. H. (2009, July). Evaluating Variability in Tumor Measurements from Same-day Repeat CT Scans of Patients with Non–Small Cell Lung Cancer 1 . Radiology. Radiological Society of North America (RSNA). http://doi.org/10.1148/radiol.2522081593 (paper)

https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

Content

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

Acknowledgements

https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT

Zhao, Binsheng, Schwartz, Lawrence H, & Kris, Mark G. (2015). Data From RIDER_Lung CT. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.U1X8A5NR

Inspiration

Advance BioMedical Image Data Science.

The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

Note : The TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules) of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.

The RIDER Lung PET-CT collection was shared to facilitate the RIDER PET/CT subgroup activities. The PET/CT subgroup was responsible for: (1) archiving de-identified DICOM serial PET/CT phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial PET/CT scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in PET/CT imaging with the goal of defining the change in PET measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using PET measurements as end points.

About the RIDER project

The Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat "coffee break," longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT] positron emission tomography [PET] CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):

• RIDER White Paper: Combined contracts report (Sept 2008) PDF

The long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the first RIDER white paper report (2006):

• RIDER White Paper: Executive Summary PDF
• RIDER White Paper: Editorial in Nature.com

A listing of State Representatives and State Senators. For more information see: http://www.cga.ct.gov/asp/menu/legdownload.asp

Catalog of high value data inventories produced by Connecticut executive branch agencies, pursuant to Connecticut General Statutes Section 4-67p. Executive branch agencies update their high value data inventories annually in December. This dataset was last collected in December of 2023. High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

Research data (mostly *.hdf5 format) which was obtained by scanning the object via Computed Tomography and generating a 3D inside view of the object with non-invasive means.This dataset is part of the digital gyroscope collection. For more information about the collection and its creation, please see the documentation dataset: Niklaus, Maria, 2021, "Information about the Gyrolog data repository", doi: 10.18419/darus-821, DaRUS.To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", doi: 10.18419/darus-gyrolog, DaRUS.

Research data (mostly *.hdf5 format) which was obtained by scanning the object via Computed Tomography and generating a 3D inside view of the object with non-invasive means.This dataset is part of the digital gyroscope collection. For more information about the collection and its creation, please see the documentation dataset: Niklaus, Maria, 2021, "Information about the Gyrolog data repository", doi: 10.18419/darus-821, DaRUS.To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", doi: 10.18419/darus-gyrolog, DaRUS.

Listing of all the medical marijuana and cannabis products registered with the State Dept. of Consumer Protection

The segmentations and models of the bones of the lower extremities were created from anonymized postmortem CT scans of the whole body originally published by Michael Kistler in the Swiss Institute for Computer Assisted Surgery Medical Image Repository (smir.ch) as open access Virtual Skeleton Database (VSD). A mirror of the VSD is available at Zenodo: 10.5281/zenodo.8270364.

However, this is a stand-alone upload and the full VSD is not required to use this upload. Further information can be found in the following publication:

Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Sci. Data 10, 763; 10.1038/s41597-023-02669-z (2023).

Post-processed 3D surface models stored as MATLAB MAT files were released as Git repository at https://github.com/MCM-Fischer/VSDFullBodyBoneModels and versioned via Zenodo: 10.5281/zenodo.8316730. The use of the MAT files is explained by examples for MATLAB and Python in the Git repository.

Usage
The CT volume data, segmentations, reconstructions and raw PLY mesh files of each subject are linked by a project file (MRML scene file) that can be opened with the open-source medical imaging software 3D Slicer (slicer.org).

This dataset consists of CT and PET-CT DICOM images of lung cancer subjects with XML Annotation files that indicate tumor location with bounding boxes. The images were retrospectively acquired from patients with suspicion of lung cancer, and who underwent standard-of-care lung biopsy and PET/CT. Subjects were grouped according to a tissue histopathological diagnosis. Patients with Names/IDs containing the letter 'A' were diagnosed with Adenocarcinoma, 'B' with Small Cell Carcinoma, 'E' with Large Cell Carcinoma, and 'G' with Squamous Cell Carcinoma.

The images were analyzed on the mediastinum (window width, 350 HU; level, 40 HU) and lung (window width, 1,400 HU; level, –700 HU) settings. The reconstructions were made in 2mm-slice-thick and lung settings. The CT slice interval varies from 0.625 mm to 5 mm. Scanning mode includes plain, contrast and 3D reconstruction.

Before the examination, the patient underwent fasting for at least 6 hours, and the blood glucose of each patient was less than 11 mmol/L. Whole-body emission scans were acquired 60 minutes after the intravenous injection of 18F-FDG (4.44MBq/kg, 0.12mCi/kg), with patients in the supine position in the PET scanner. FDG doses and uptake times were 168.72-468.79MBq (295.8±64.8MBq) and 27-171min (70.4±24.9 minutes), respectively. 18F-FDG with a radiochemical purity of 95% was provided. Patients were allowed to breathe normally during PET and CT acquisitions. Attenuation correction of PET images was performed using CT data with the hybrid segmentation method. Attenuation corrections were performed using a CT protocol (180mAs,120kV,1.0pitch). Each study comprised one CT volume, one PET volume and fused PET and CT images: the CT resolution was 512 × 512 pixels at 1mm × 1mm, the PET resolution was 200 × 200 pixels at 4.07mm × 4.07mm, with a slice thickness and an interslice distance of 1mm. Both volumes were reconstructed with the same number of slices. Three-dimensional (3D) emission and transmission scanning were acquired from the base of the skull to mid femur. The PET images were reconstructed via the TrueX TOF method with a slice thickness of 1mm.

The location of each tumor was annotated by five academic thoracic radiologists with expertise in lung cancer to make this dataset a useful tool and resource for developing algorithms for medical diagnosis. Two of the radiologists had more than 15 years of experience and the others had more than 5 years of experience. After one of the radiologists labeled each subject the other four radiologists performed a verification, resulting in all five radiologists reviewing each annotation file in the dataset. Annotations were captured using Labellmg. The image annotations are saved as XML files in PASCAL VOC format, which can be parsed using the PASCAL Development Toolkit: https://pypi.org/project/pascal-voc-tools/. Python code to visualize the annotation boxes on top of the DICOM images can be downloaded here.

Two deep learning researchers used the images and the corresponding annotation files to train several well-known detection models which resulted in a maximum a posteriori probability (MAP) of around 0.87 on the validation set.

Connecticut address point dataset used for locating 9-1-1 calls. The address point feature class format is derived from National Emergency Number Association (NENA) and Federal Geographic Data Committee (FGDC) addressing standards. All address components, like address number, streets name and unit number, are broken up into their individual components to enable maximum flexibility for use. Fields within feature class should be able to accommodate all addresses within the state of Connecticut. The source for the addresses is primarily derived from municipal parcel data and other municipal sources.

This dataset contains statewide CAMA information for the parcels in Connecticut, created by the GIS office in accordance with CGS Sec. 4d-90-92 and 7-100L. It is part of the 2024 data collection effort, which involved gathering CAMA data from all municipalities through the Council of Governments. The parcel layer is provided as a zipped folder containing a File Geodatabase, encompassing information from all 169 towns organized into a seamless parcel layer. The CAMA dataset include details about real property within Connecticut towns, which can be linked to the parcel data using a GIS software. The linking is facilitated through a designated column called ‘link’ that contains unique codes for each town and the designated values provided by the assessors and COGs. While the data was gathered from Connecticut towns and submitted to CT OPM by the COGs, it’s important to note that not all towns adhered to the established schema. As a result, some attribute names, primary and secondary keys, naming conventions, and file formats were inconsistent. Cleaning and reorganization were performed to align the data with the state schema, though some limitations remain. This file was generated on 09/28/2024 from data collected throughout 2024. Additional Note: Some towns were unable to verify which entries were suppressed pursuant to Connecticut General Statute Sec. 1-217. As a result, all related information has been fully suppressed. The owner and co-owner fields have been replaced with "Current Owner" and "Current Co-Owner," respectively, and the mailing address has been updated to reflect the location address.

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

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

MIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR). Pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all COVID positive thoracic computed tomography (CT) imaging studies in a labeling schema coordinated with other international consensus panels and COVID data annotation efforts.

Results

MIDRC-RICORD dataset 1a consists of 120 thoracic computed tomography (CT) scans from four international sites annotated with detailed segmentation and diagnostic labels.

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

Data Abstract

1. 120 Chest CT examinations (axial series only, any protocol).

2. Annotations comprised of

• a) Detailed segmentation of affected regions;
• b) Image-level labels (Infectious opacity, Infectious TIB/micronodules, Infectious cavity, Noninfectious nodule/mass, Atelectasis, Other noninfectious opacity)
• c) Exam-level diagnostic labels (Typical, Indeterminate, Atypical, Negative for pneumonia, Halo sign, Reversed halo sign, Reticular pattern w/o parenchymal opacity, Perilesional vessel enlargement, Bronchial wall thickening, Bronchiectasis, Subpleural curvilinear line, Effusion, Pleural thickening, Pneumothorax, Pericardial effusion, Lymphadenopathy, Pulmonary embolism, Normal lung, Infectious lung disease, Emphysema, Oncologic lung disease, Non-infectious inflammatory lung disease, Non-infectious interstitial, Fibrotic lung disease, Other lung disease)
• d) Exam-level procedure labels (With IV contrast, Without IV contrast, QA- inadequate motion/breathing, QA- inadequate insufficient inspiration, QA- inadequate low resolution, QA- inadequate incomplete lungs, QA- inadequate wrong body part/modality, Endotracheal tube, Central venous/arterial line, Nasogastric tube, Sternotomy wires, Pacemaker, Other support apparatus).

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, 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.

Connecticut State Archives Archival Record Group (RG) #069:050, Noble (William H. and Henrietta) Pension Applications

General William H. Noble and his daughter Henrietta M. Noble, Pension Agents in Bridgeport, assisted veterans and their descendants to secure pensions from the United States Government. The collection includes correspondence and official papers that document their work with veterans of the Civil War and Spanish American War. The files are arranged alphabetically by veteran’s name.

The database contains the following information: veteran’s name, rank, pension file application number, date enlisted, date discharged, and military unit.

People may request a copy of a file by contacting the staff of the History & Genealogy Unit by telephone (860) 757-6580 or email. When requesting a copy of a record, please include at least the name of the individual, date, and residence.

Abbreviations of Connecticut Military Branch of Service:

· CLB – Connecticut Light Battery

· CVA – Connecticut Volunteer Artillery

· CVC – Connecticut Volunteer Cavalry

· CVHA – Connecticut Volunteer Heavy Artillery

· CVI – Connecticut Volunteer Infantry

· CVLB – Connecticut Volunteer Light Battery

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.

As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a program in that calendar year and month. A recipient may have received benefits from multiple programs in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. NOTE: On 11/30/2018 the counts were revised because of a change in the way active recipients were counted in one source system.

96 Global export shipment records of Ct Scanner with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.