This document presents the standard starting point language to use when drafting a formal data sharing agreement between a City entity and either another City entity or an outside party when two parties seek to share non-public data with one another. The document outlines the following major concerns:Parties to the agreementPurpose of the data sharing effort Period of the agreementDescription of the data to be sharedTiming and frequency of updates to the shared dataPoint(s) of contactCustodial responsibilitiesMethod of data transferPublication ReviewOther City terms and conditions This version 1.1 makes minor corrections of language originally formalized by the City's Data Governance Committee in June of 2020. Note that a data sharing agreement is not final or authorized without appropriate signatures from all parties represented by the agreement.

This Data Access Agreement (DAA) is freely available to use and is intended for use where data is accessed within a Trusted Research Environment (TRE) for the purposes of research and development for the public good. The DAA has been developed by the TRE Legal Toolkit Action Force of the Pan UK Data Governance Steering Group. The Pan-UK Data Governance Steering Group is a working Group of the UK Health Data Research Alliance representing data custodians and policymakers across the four nations. The Steering Group is focused on simplifying and streamlining data access governance processes. The DAA terms and conditions should not be modified. The annexes are customisable to allow for differences between TREs. We wish to encourage widespread adoption of this template and it is freely available to use. If you do plan to adopt this template or would like to discuss any queries please get in touch with: Rachel Brophy, Information and Research Governance manager HDR UK (rachel.brophy@hdruk.ac.uk) cc: informationgovernance@hdruk.ac.uk Please see version control document for details of changes.

One of the well-established methods used to ease data sharing between organisations and even teams within organisations is to use standards for data structure, metadata and interfaces. Standards are a form of agreement, as are MoUs, charters, deeds, licences, rules of the road and even the definitions for words. Man y of these other sorts of agreements are also important for data sharing communities too. In this paper we look to improve the efficiency of dealing with different forms of agreement within a data sharing scenario by presenting a prototype agreements ontology which models agreements themselves as ¿things¿ and the relationships between them and between them and data and them and agents. Having an agreements ontology allows us to start automating tasks that require knowledge of them. This may take the form of data repositories that can make intelligent choices about how to deliver or with old data without human intervention. We position this ontology as a 'middle' ontology, that is one which specializes well-known, abstract, upper ontologies and is able to be used fairly widely but is expected to be used in particular contexts in conjunction with detailed, domain-specific, lower ontologies. We have relied on existing agent, data manipulation, and metadata ontologies where possible and as such we specialise the ORG and FOAF ontologies, the PROV ontology and DCAT and ODRS ontologies for those areas respectively. This paper and ontology supports work that we report elsewhere at SciDataCon2016, namely The Role of Social Architecture in Information Infrastructure (Box & Lemon) and Describing and Automating Requirements within Licenses and their Resolutions (Car & Stenson).

This dataset contains all of the current parcels that are currently under an Open Space Use Agreement between the owners of the parcel and the County of Albemarle. These agreements limit construction and development activity on the property owner's land, and lasts from 4 to 10 years. For more information on any particular agreement, contact the Real Estate division of the County of Albemarle's Finance Department.

AWT is a component of the Verification and Information Exchange Workload System (VIEWS). It is an online application which replicates the reimbursable agreement documents for review, approval and signature. AWT is available within the agency nationwide with users in various components within Headquarters, Office of Central Operations and Regional Offices across the country.

The Agreement Workflow Tool (AWT) is a role-based Intranet application used for processing SSA's Reimbursable Agreements according to SSA's standards. AWT provides project coordinators with the functionality to create a cost estimate, SSA-1033, and store multiple versions for use in subsequent workflows. AWT also provides project coordinators with functionality to Create, Renew, and Amend the SSA-1235 and SSA-40 Financial Documents. The application also provides functionality to generate agreement related reports, search for an agreement, view agreement history, and store agreement related documentation. AWT interfaces with SSN Verification (CBSV) to ensure that the external CBSV customers have active agreements with the SSA. AWT also interfaces with the Data Exchange Inventory (DEXI) application to update the data exchange inventory information and provide SSA users with a link to the reimbursable agreement documentation. AWT provides the Regional Office Data Exchange Coordinators (DECs) with the ability to create and modify tracking records for the non-reimbursable agreements between SSA and the States or State Agencies. AWT is developed with ColdFusion and DB2. Users of AWT include DCS, DCO (including the Regional Offices), OGC, DCBFQM, OCO, and ODX.

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset reflects the boundaries of those Indigenous Land Use Agreements (ILUA) that have entered the notification process or have been registered and placed on the Register of Indigenous Land Use Agreements (s199A, Native Title Act; Commonwealth). This is a national dataset. Spatial attribution includes National Native Title Tribunal number, Name, Agreement Type, Proponent, Area and Registration Date. Products using this data should acknowledge the National Native Title Tribunal as the data source. Dataset History Lineage: Created by the National Native Title Tribunal in 1998 and continuously updated and maintained. Positional accuracy: 0.1 m Attribute accuracy: Attributes are maintained continuously and should at all times reflect the primary detail as contained within the Register of ILUA's. Logical Consistency: Technical or unintentional overlaps between boundaries may arise within this dataset. Technical overlaps include portions of boundaries of determinations that are intended to abut but which overlap. These overlaps may be caused by changes in source datasets used to create initial application boundaries or by differing interpretations of determination descriptions. Part of the maintenance program of this dataset is the identification and removal of such technical overlaps. Completeness: Ongoing https://data.gov.au/data/dataset/eb8caa51-a883-4e87-907d-fea1a4a054f1

Contracts and modifications awarded by the City of Chicago since 1993. This data is currently maintained in the City’s Financial Management and Purchasing System (FMPS), which is used throughout the City for contract management and payment. Legacy System Records: Purchase Order/Contract Numbers that begin with alpha characters identify records imported from legacy systems. Records with a null value in the Contract Type field were imported from legacy systems. "Comptroller-Other" Contract Type: Some records where the Contract Type is "COMPTROLLER-OTHER" are ordinance-based agreements and may have start dates earlier than 1993. Depends Upon Requirements Contracts: If the contract Award Amount is $0, the contract is not cancelled, and the contract is a blanket contract, then the contract award total Depends Upon Requirements. A Depends Upon Requirements contract is an indefinite quantities contract in which the City places orders as needed and the vendor is not guaranteed any particular contract award amount.

Blanket vs. Standard Contracts: Only blanket contracts (contracts for repeated purchases) have FMPS end dates. Standard contracts (for example, construction contracts) terminate upon completion and acceptance of all deliverables. These dates are tracked outside of FMPS.

Negative Modifications: Some contracts are modified to delete scope and money from a contract. These reductions are indicated by negative numbers in the Award Amount field of this dataset.

Data Owner: Procurement Services. Time Period: 1993 to present. Frequency: Data is updated daily.

Licensing agreements for images provided for publication in the eFlora.

Beginning March 1, 2022, the "COVID-19 Case Surveillance Public Use Data" will be updated on a monthly basis. This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data. CDC has three COVID-19 case surveillance datasets: COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements) COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements) COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (32 data elements) The following apply to all three datasets: Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf. Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. Some data cells are suppressed to protect individual privacy. The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the previously updated datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured. Datasets are updated monthly. Datasets are created using CDC’s operational Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy. For more information about data collection and reporting, please see https://wwwn.cdc.gov/nndss/data-collection.html For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html Overview The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification. The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported volun

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish. In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package. Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records. It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record. ***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

The President’s Malaria Initiative (PMI) is a U.S. Government initiative designed to reduce malaria deaths and illnesses in target countries in sub-Saharan Africa with a long-term vision of a world without malaria. This asset contains six excel datasets that include information related to pharmaceuticals products used for malaria treatment, commodity shipments during the 2013 fiscal year, and the status of stocks and delivery plans covering fiscal years 2013 to 2018. Four datasets are unrestricted and open for general public use: - PMI GHSC_Product_Master - PMI Commodity Shipments under DELIVER - PMI Commodity Shipments under PSM, and - PMI Product Status (PPRMmFY13-FY2018) Two datasets are with restrictions and not open for general public use: - PMI Product Harmonization_GF inputs - Restricted Public. Disclosure is prohibited unless approved by GC and subject to a Data Use Agreement. - PMI Stockout Rates - Non-Public. Disclosure outside USAID is prohibited. The data asset also includes summary reports in pdf format both on procurement and on malaria treatment.

The Phantom of Bern: repeated scans of two volunteers with eight different combinations of MR sequence parameters

The Phantom of Bern consists of eight same-session re-scans of T1-weighted MRI with different combinations of sequence parameters, acquired on two healthy subjects. The subjects have agreed in writing to the publication of these data, including the original anonymized DICOM files and waving the requirement of defacing. Usage is permitted under the terms of the data usage agreement stated below.

CONTENT

The BIDS directory is organized as follows:

└── PhantomOfBern/
  ├─ code/
  │
  ├─ derivatives/
  │ ├─ dldirect_v1-0-0/
  │ │ ├─ results/ # Folder with flattened subject/session inputs and outputs of DL+DiReCT
  │ │ └─ stats2table/ # Folder with tables summarizing all DL+DiReCT outputs
  │ ├─ freesurfer_v6-0-0/
  │ │ ├─ results/ # Folder with flattened subject/session inputs and outputs of freesurfer
  │ │ └─ stats2table/ # Folder with tables summarizing all freesurfer outputs
  │ └─ siena_v2-6/
  │   ├─ SIENA_results.csv # Siena's main output
  │   └─ ... # Flattened subject/session inputs and outputs of SIENA
  │
  ├─ sourcedata/
  │ ├─ POBHC0001/
  │ │ └─ 17473A/
  │ │   └─ ... # Anonymized DICOM folders
  │ └─ POBHC0002/
  │   └─ 14610A/
  │    └─ ... # Anonymized DICOM folders
  │
  ├─ sub-<label>/
  │ └─ ses-<label>/
  │   └─ anat/ # Folder with scan's json and nifti files
  ├─ ...

ACKNOWLEDGEMENT

The dataset can be cited as:

M. Rebsamen, D. Romascano, M. Capiglioni, R. Wiest, P. Radojewski, C. Rummel. The Phantom of Bern:
repeated scans of two volunteers with eight different combinations of MR sequence parameters.
OpenNeuro, 2023.

If you use these data, please also cite the original paper:

M. Rebsamen, M. Capiglioni, R. Hoepner, A. Salmen, R. Wiest, P. Radojewski, C. Rummel. Growing importance
of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters.
Journal of Neuroradiology, 2023.

DATA USE AGREEMENT

The Phantom of Bern is distributed under the following terms, to which you agree by downloading and/or using the dataset:

1. Any intentional identification of a subject or disclosure of his or her confidential information violates the promise of confidentiality given to the providers of the information. Therefore, all users of the dataset agree:

• To use these datasets solely for research and development or statistical purposes and not for investigation of specific subjects

• To make no use of the identity of any subject discovered inadvertently, and to advise the providers of any such discovery (crummel@web.de)

1. When publicly presenting any results or algorithms that benefited from the use of the Phantom of Bern, you should acknowledge it, see above. Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from the Phantom of Bern data should cite the publications listed above.

2. Redistribution of data (complete or in parts) in any manner without explicit inclusion of this data use agreement is prohibited.

3. Usage of the data for testing commercial tools is explicitly allowed. Usage for military purposes is prohibited.

4. The original collector and provider of the data (see acknowledgement) and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.

FUNDING

This work was supported by the Swiss National Science Foundation under grant numbers 204593 (ScanOMetrics) and CRSII5_180365 (The Swiss-First Study).

Fair Political Practices Commision (FPPC) Form 802 reporting data is included.Note: Date type fields that contain both date and time will be reflected in UTC time in the downloaded data and in the APIs, but through the Open Data Portal application these same fields will be displayed as the user’s local time. String type fields that contain time only as text, will be viewed and downloaded in US/Pacific time.

The Department of Planning, Lands and Heritage data licensing agreement for the use of digital information acquired from Data WA Show full description

An "indigenous land use agreement" (ILUA) is a voluntary, legally binding agreement about the use and management of land or waters, made between one or more native title groups and non-native title interest holders in the ILUA area (such as grantee parties, pastoralists or governments).

Information on the current Across Government ICT Contracts, suppliers and how these contracts are to be used with the South Australian Government.

Objectives: We sought to work collaboratively with public health stakeholders who use evidence in their work to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities.

Methods: We undertook three sequential stakeholder workshops with participants from local and central government, public health teams, Health and Social Care Partnerships, the third sector, organisations which support data-intensive research, and public representatives from across Scotland. The workshops were informed by a scoping review on use of evidence in public health policy and practice, searching Medline, Scopus, SSCI, and key institutional websites, and by three case studies of existing cross-sectoral linkage projects.

Details of data collection: The data collection comprises de-identified transcripts of stakeholder workshops and a copy of the visual map produced as part of the workshops. Stakeholders comprised people We held workshops to bring together people working in public health practice; in policy sectors potentially relevant to health; and in information governance, infrastructure and/or support for data and research; as well as a number of public representatives. Potential attendees were identified through a stakeholder mapping exercise with the project advisory group, followed by review of relevant organisational websites and advice from gatekeeper organisations such as Administrative Data Scotland.

Background Secondary data from different sectors can provide unique insights into the social, environmental, economic, and political determinants of health. This is especially pertinent in the context of whole-systems approaches to public health, which typically combine cross-sectoral collaboration with the application of theoretical insights from systems science. However, sharing and linkage of data between different sectors to inform healthy public policy is still relatively rare. Previous research has documented the perspectives of researchers and members of the public on data sharing, especially healthcare data, but has not engaged with decision-makers working in public health practice and public policy. Objective(s) We sought to work collaboratively with public health stakeholders who use evidence in their work to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities. Methods We undertook three sequential stakeholder workshops with participants from local and central government, public health teams, Health & Social Care Partnerships, the third sector, organisations which support data-intensive research, and public representatives from across Scotland. The workshops were informed by a scoping review on use of evidence in public health policy and practice, searching Medline, Scopus, SSCI, and key institutional websites, and by three case studies of existing cross-sectoral linkage projects. Findings were synthesised using thematic analysis. Setting and scope Scotland; public and third sector data.

The National Survey of Family Growth (NSFG) gathers information on family life, marriage and divorce, pregnancy, infertility, use of contraception, and men's and women's health. The survey results are used by the U.S. Department of Health and Human Services and others to plan health services and health education programs, and to do statistical studies of families, fertility, and health. Years included: 1973, 1976, 1982, 1988, 1995, 2002, 2006-2010; Data use agreement at time of file download:

This dataset includes the results of the pilot activity that Public Services and Procurement Canada undertook as part of Canada’s 2018-2020 National Action Plan on Open Government. The purpose is to demonstrate the usage and implementation of the Open Contracting Data Standard (OCDS). OCDS is an international data standard that is used to standardize how contracting data and documents can be published in an accessible, structured, and repeatable way. OCDS uses a standard language for contracting data that can be understood by all users. ###What procurement data is included in the OCDS Pilot? Procurement data included as part of this pilot is a cross-section of at least 250 contract records for a variety of contracts, including major projects. ###Methodology and lessons learned The Lessons Learned Report documents the methodology used and the lessons learned during the process of compiling the pilot data.