This dataset lists suggestions received through the CT Open Data Dataset Suggestion survey here: https://data.ct.gov/stories/s/eivh-c3ze.

Documentation for working with the open data catalog, datasets, visualizations, and filters.

A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

DAPSTOM (integrated database and portal for fish stomach records) is an ongoing initiative (supported by Defra and the EU) to digitise and make available fish stomach content records spanning the past 100 years. In this latest iteration (Version 6.3) an additional 26,767 records for 122,137 individual predator stomachs have been added to the dataset (including 31,488 cod stomachs), bringing the total up to 283,121 records from 481,476 stomachs and 741 distinct research cruises/sampling campaigns. Data are available for 210 predator species and a huge swathe of the Northeast Atlantic, but particularly the North Sea, Celtic Sea, Irish Sea and area around Spitzbergen. Records span the period 1836 to 2023, and the database encompasses individuals ranging in size from 0.1 cm (a herring larva) to 768 cm for a basking shark caught in 1947.

The DAPSTOM database (version 1) was originally created in 2007. It has been used to parameterise multispecies fisheries models and to develop ecosystem indicators, with a focus on ‘food-webs’, as well as basic investigations into the underlying biology of particular species. Most datasets were derived from scientists log-books or reports contained within the Cefas archive, others were donated by ‘partners’ or were digitised from published peer-reviewed papers, with specific relevance to the British Isles. This update to DAPSTOM has been funded via the European Union under project “SC10 – Study on stomach content of fish to update databases and analyse possible changes in diet or food webs interactions” (EASME/EMFF/2018/011). The intention is that the DAPSTOM Version 6.3 database will be made searchable and freely available to users via a dedicated R-shiny interface.

Assignment Topic: In this assignment, you will download the datasets provided, load them into a database, write and execute SQL queries to answer the problems provided, and upload a screenshot showing the correct SQL query and result for review by your peers. A Jupyter notebook is provided in the preceding lesson to help you with the process.

This assignment involves 3 datasets for the city of Chicago obtained from the Chicago Data Portal:

1. Chicago Socioeconomic Indicators

This dataset contains a selection of six socioeconomic indicators of public health significance and a hardship index, by Chicago community area, for the years 2008 – 2012.

1. Chicago Public Schools

This dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year.

1. Chicago Crime Data

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days.

Instructions:

1. Review the datasets

Before you begin, you will need to become familiar with the datasets. Snapshots for the three datasets in .CSV format can be downloaded from the following links:

Chicago Socioeconomic Indicators: Click here

Chicago Public Schools: Click here

Chicago Crime Data: Click here

NOTE: Ensure you have downloaded the datasets using the links above instead of directly from the Chicago Data Portal. The versions linked here are subsets of the original datasets and have some of the column names modified to be more database friendly which will make it easier to complete this assignment. The CSV file provided above for the Chicago Crime Data is a very small subset of the full dataset available from the Chicago Data Portal. The original dataset is over 1.55GB in size and contains over 6.5 million rows. For the purposes of this assignment you will use a much smaller sample with only about 500 rows.

1. Load the datasets into a database

Perform this step using the LOAD tool in the Db2 console. You will need to create 3 tables in the database, one for each dataset, named as follows, and then load the respective .CSV file into the table:

CENSUS_DATA

CHICAGO_PUBLIC_SCHOOLS

CHICAGO_CRIME_DATA

This web page leads to a database of images and information about the 150 major impact craters on Ganymede and is updated semi-regularly based on continuing analysis of Voyager 2 images.

Database portal containing replicate experiments of different assays and samples

1. Website usage instructions2. Website usage case instructions3. Website unit introduction

This web page leads to a database of images and information about the 150 major impact craters on Callisto and is updated semi-regularly based on continuing analysis of Voyager images.

The MN Public Health Data Access portal, maintained by the Minnesota Department of Health (MDH), provides data on over 20 health and environment topics. Data are accessible through charts, tables, and maps, and also may be downloaded from MDH's website. Users may use these data to inform state and local planning and policy, grant writing, research, and more.

Tags are part of the information, commonly called metadata, that can be added when creating new items, authoring maps and apps, or creating new groups in your organization. They can be added to any item, can be edited, and are a useful way to boost search results and find specific content.Without proper forethought, tagging data will quickly become a subjective process with a mess of inconsistent tags existing within an organization. When sharing data publicly over a multi-organizational open data platform such as the Florida Geospatial Open Data Portal, these tags may be incompatible with tags used by other organizations.This webpage seeks to provide guidance to State of Florida organizations that participate in the Florida Geospatial Open Data Portal by highlighting how tagging data works in the ArcGIS Online platform, providing best practices for getting started tagging data in your own organization, and explaining how tagging works with the Florida Geospatial Open Data Portal.

The GCMD database holds more than 30,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences. The mission of the GCMD is to (1) Assist the scientific community in the discovery of Earth science data, related services, and ancillary information (platforms, instruments, projects, data centers/service providers); and (2) Provide discovery/collection-level metadata of Earth science resources and provide scientists a comprehensive and high quality database to reduce overall expenditures for scientific data collection and dissemination.

This dataset contains data collected during a study "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).

This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.

It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.

Purpose of the expert assessment The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.

Methodology Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching. To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point. Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in Lněnička et al. (2021)* is expected to be met. When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers. *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605 *Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.

Test procedure (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.

Description of the data in this data set Sheet#1 "comparison_overall" provides results by portal Sheet#2 "comparison_category" provides results by portal and category Sheet#3 "category_subcategory" provides list of categories and its elements

Format of the file .xls

Licenses or restrictions CC-BY

For more info, see README.txt

AI Generated Summary: Datos.gob.do is the Dominican Republic's National Open Data Portal, designed for sharing, connecting, and visualizing datasets. It provides users with easy access to the Governmental Open Data Catalog, promoting free access, reuse, and redistribution of public data to improve quality of life, sustainable development, and government transparency. About: Datos.gob.do, the National Open Data Portal, is the digital platform that functions as a means to share, connect, and visualize datasets, also being the virtual space through which users have access to the Governmental Open Data Catalog, with ease of search, access, obtaining, reuse, free use, and redistribution. The Government of the Dominican Republic has a firm commitment to enhancing the quality of life of people and sustainable development, for which guaranteeing the right to access public information and transparency in the management of public resources is a cross-cutting and paramount axis, so this tool aims to allow people to have access to the open data produced by public institutions, thus allowing its easy reuse by citizens, in addition to achieving the transformation of data to exchange and cross between databases, as well as develop technological tools. The launch of the new National Open Data Portal, in accordance with the first National Open Data Policy and the Action Plan for the Opening of Open Data, is part of the first commitment of the V Action Plan of the Dominican Republic to the Alliance for Open Government 2022-2024. Translated from Spanish Original Text: Datos.gob.do El Portal Nacional de Datos Abiertos es la plataforma digital que funciona como medio para compartir, conectar y visualizar conjuntos de datos, siendo a su vez el espacio virtual a través del cual las personas usuarias tienen acceso al Catálogo Gubernamental de Datos Abiertos, con facilidad de búsqueda, acceso, obtención, reutilización, uso gratuito y redistribución. El Gobierno de la República Dominicana tiene un firme compromiso con potenciar la calidad de vida de las personas y el desarrollo sostenible, para ello garantizar el derecho de acceder a la información pública y la transparencia en la gestión de los recursos públicos es un eje transversal y principalísimo, por lo que esta herramienta tiene como objetivo que las personas puedan disponer de los datos abiertos que producen las instituciones públicas, permitiendo así su fácil reutilización por parte de la ciudadanía, además de lograr transformar los datos para intercambiar y cruzar entre bases de datos, así como desarrollar herramientas tecnológicas. La puesta en marcha del nuevo Portal Nacional de Datos Abiertos, acorde con la primera Política Nacional de Datos Abiertos y al Plan de Acción para la Apertura de Datos Abiertos, es parte del primer compromiso del V Plan de Acción de la República Dominicana ante la Alianza para el Gobierno Abierto 2022-2024.

These instructional videos walk users through the portal and its different features.

The Texas Open Data Portal Resource Guide 2025 is produced by the Texas Department of Information Resources to assist publishing organizations in their use of the Open Data Portal. While not exhaustive, this document serves as a guide in establishing an open data governance framework, creating an open data inventory, and publishing open data in an efficient and standardized manner.

The specifications and guidelines in this Data Management Plan will improve data consistency and availability of information. It will ensure that all levels of government and the public have access to the most up-to-date information; reduce or eliminate overlapping data requests and redundant data maintenance; ensure metadata is consistently created; and ensure that data services can be displayed by the consumer with the output of its choice.

This project includes a pdf capture of a webpage and the underlying data for the visualizations in both csv and Tableau formats.On July 28-30, 2017, the VA Office of Enterprise Integration and the University of California Agriculture and Natural Resources (UCANR) convened a 48-hour collaborative event at the Urban Hive in Sacramento, California, to encourage the development of innovative solutions to spark entrepreneurship and bring together the seemingly disparate worlds of software development, commercial farming, and Veterans.Data about Veteran farmers by county was also used at Tableau’s Student Data Hackathon on July 31, 2018, where Washington D.C. area college students, who are Veterans, learned about Tableau products using data from VA’s Open Data and the Bureau of Labor Statistics to build data analytics skills creating data visualizations.

A. SUMMARY This dataset is used to report on public dataset access and usage within the open data portal. Each row sums the amount of users who access a dataset each day, grouped by access type (API Read, Download, Page View, etc).

B. HOW THE DATASET IS CREATED This dataset is created by joining two internal analytics datasets generated by the SF Open Data Portal. We remove non-public information during the process.

C. UPDATE PROCESS This dataset is scheduled to update every 7 days via ETL.

D. HOW TO USE THIS DATASET This dataset can help you identify stale datasets, highlight the most popular datasets and calculate other metrics around the performance and usage in the open data portal.

Please note a special call-out for two fields: - "derived": This field shows if an asset is an original source (derived = "False") or if it is made from another asset though filtering (derived = "True"). Essentially, if it is derived from another source or not. - "provenance": This field shows if an asset is "official" (created by someone in the city of San Francisco) or "community" (created by a member of the community, not official). All community assets are derived as members of the community cannot add data to the open data portal.

The home of Medicaid and CHIP open data provided by the Federal Government. Conduct research and design data visualizations using open data from Medicaid and the Children's' Health Insurance Program (CHIP) Data available on the following categories: • Drug Pricing and Payment • Enrollment • Quality • Eligibility • State Drug Utilization • Uncategorized