The VHA Data Sharing Agreement Repository serves as a centralized location to collect and report on agreements that share VHA data with entities outside of VA. It provides senior management an overall view of existing data sharing agreements; fosters productive sharing of health information with VHA's external partners; and streamlines data acquisition to improve data management responsibilities overall. Agreements that VHA has established with entities within the VA are not candidates for this Repository.

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

KL3M Data Project

Note: This page provides general information about the KL3M Data Project. Additional details specific to this dataset will be added in future updates. For complete information, please visit the GitHub repository or refer to the KL3M Data Project paper.

  Description

This dataset is part of the ALEA Institute's KL3M Data Project, which provides copyright-clean training resources for large language models.

  Dataset Details

Format: Parquet… See the full description on the dataset page: https://huggingface.co/datasets/alea-institute/kl3m-data-edgar-agreements.

Prepaid account agreement data, which contain general terms and conditions, pricing, and fee information, that issuers submit to the Bureau under the terms of the Prepaid Rule. Data is refreshed nightly.

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.

Please download the full version of the dataset from Zenodo, here.

Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled by The Atticus Project to identify 41 categories of important clauses that lawyers look for when reviewing contracts.

We tested CUAD v1 against ten pretrained AI models and published the results on arXiv here.

Code for replicating the results, together with the model trained on CUAD, is published on Github here.

Over the last decade, there have been significant changes in data sharing policies and in the data sharing environment faced by life science researchers. Using data from a 2013 survey of over 1600 life science researchers, we analyze the effects of sharing policies of funding agencies and journals. We also examine the effects of new sharing infrastructure and tools (i.e., third party repositories and online supplements). We find that recently enacted data sharing policies and new sharing infrastructure and tools have had a sizable effect on encouraging data sharing. In particular, third party repositories and online supplements as well as data sharing requirements of funding agencies, particularly the NIH and the National Human Genome Research Institute, were perceived by scientists to have had a large effect on facilitating data sharing. In addition, we found a high degree of compliance with these new policies, although noncompliance resulted in few formal or informal sanctions. Despite the overall effectiveness of data sharing policies, some significant gaps remain: about one third of grant reviewers placed no weight on data sharing plans in their reviews, and a similar percentage ignored the requirements of material transfer agreements. These patterns suggest that although most of these new policies have been effective, there is still room for policy improvement.

Existing Contracts Register for awarded contracts over £5,000. An extract of all published contract awards starting from 01 April 2017. The data names the buyer and the awarded suppliers, plus information on the value and duration of the contract itself. This is a work in progress - by improving data quality and maintaining compliance with procurement regulations, the Council is working towards a complete dataset. Other details about Contracts shown in this dataset may be available on the ProContract website (source link shown below). That website may for example, also provide information on suppliers for Contracts that have more than one supplier. The data is updated quarterly. Data source: Procurement Lincolnshire, Lincolnshire County Council. For any enquiries about this publication contact procontract.support@lincolnshire.gov.uk

understanding agreements

This Agreement outlines the terms under which the Institution and its affiliated Researchers may use any data, findings, or derivatives collected during the course of research on The Ridges Sanctuary property.

Data and code belonging to the manuscript:

Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers

Abstract

Transformative agreements have become an important strategy in the transition to open access, with almost 1,200 such agreements registered by 2025. Despite their prevalence, these agreements suffer from important transparency limitations, most notably article-level metadata indicating which articles are covered by these agreements. Typically, this data is available to libraries but not openly shared, making it difficult to study the impact of these agreements. In this paper, we present a novel, open, replicable method for analyzing transformative agreements using open metadata, specifically the Journal Checker tool provided by cOAlition S and OpenAlex. To demonstrate its potential, we apply our approach to a subset of publications funded by the Dutch Research Council (NWO) and its health research counterpart ZonMw. In addition, the results of this open method are compared with the actual publisher data reported to the Dutch university library consortium UKB. This validation shows that this open method accurately identified 89% of the publications covered by transformative agreements, while the 11% false positives shed an interesting light on the limitations of this method. In the absence of hard, openly available article-level data on transformative agreements, we provide researchers and institutions with a powerful tool to critically track and evaluate the impact of these agreements.

This dataset contains the following files:

• Dataset.csv - Data set of unique DOIs (n = 6,610) enriched with data from Crossref, Unpaywall, OpenAlex and the Journal Checker Tool.
• Data dictionary.csv - description of the data in the dataset, its type and sources.
• Google_Apps_Script.js - Google Apps Script for retrieving information from the Journal Checker Tool API.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

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. (33 data elements)

• 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 current 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 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://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.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 aut

Bilateral Labor Agreements Dataset and additional replication files for "Immigration and International Law"

Abstract and poster of paper 0949 presented at the Digital Humanities Conference 2019 (DH2019), Utrecht , the Netherlands 9-12 July, 2019.

US Broadband Usage Across Counties

Utilizing Microsoft's Data to Estimate Access

By Amber Thomas [source]

About this dataset

This dataset provides an estimation of broadband usage in the United States, focusing on how many people have access to broadband and how many are actually using it at broadband speeds. Through data collected by Microsoft from our services, including package size and total time of download, we can estimate the throughput speed of devices connecting to the internet across zip codes and counties.

According to Federal Communications Commission (FCC) estimates, 14.5 million people don't have access to any kind of broadband connection. This data set aims to address this contrast between those with estimated availability but no actual use by providing more accurate usage numbers downscaled to county and zip code levels. Who gets counted as having access is vastly important -- it determines who gets included in public funding opportunities dedicated solely toward closing this digital divide gap. The implications can be huge: millions around this country could remain invisible if these number aren't accurately reported or used properly in decision-making processes.

This dataset includes aggregated information about these locations with less than 20 devices for increased accuracy when estimating Broadband Usage in the United States-- allowing others to use it for developing solutions that improve internet access or label problem areas accurately where no real or reliable connectivity exists among citizens within communities large and small throughout the US mainland.. Please review the license terms before using these data so that you may adhere appropriately with stipulations set forth under Microsoft's Open Use Of Data Agreement v1.0 agreement prior to utilizing this dataset for your needs-- both professional and educational endeavors alike!

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How to use the dataset

How to Use the US Broadband Usage Dataset

This dataset provides broadband usage estimates in the United States by county and zip code. It is ideally suited for research into how broadband connects households, towns and cities. Understanding this information is vital for closing existing disparities in access to high-speed internet, and for devising strategies for making sure all Americans can stay connected in a digital world.

The dataset contains six columns: - County – The name of the county for which usage statistics are provided. - Zip Code (5-Digit) – The 5-digit zip code from which usage data was collected from within that county or metropolitan area/micro area/divisions within states as reported by the US Census Bureau in 2018[2].
- Population (Households) – Estimated number of households defined according to [3] based on data from the US Census Bureau American Community Survey's 5 Year Estimates[4].
- Average Throughput (Mbps)- Average Mbps download speed derived from a combination of data collected anonymous devices connected through Microsoft services such as Windows Update, Office 365, Xbox Live Core Services, etc.[5]
- Percent Fast (> 25 Mbps)- Percentage of machines with throughput greater than 25 Mbps calculated using [6]. 6) Percent Slow (< 3 Mbps)- Percentage of machines with throughput less than 3Mbps calculated using [7].

Research Ideas

• Targeting marketing campaigns based on broadband use. Companies can use the geographic and demographic data in this dataset to create targeted advertising campaigns that are tailored to individuals living in areas where broadband access is scarce or lacking.
• Creating an educational platform for those without reliable access to broadband internet. By leveraging existing technologies such as satellite internet, media streaming services like Netflix, and platforms such as Khan Academy or EdX, those with limited access could gain access to new educational options from home.
• Establishing public-private partnerships between local governments and telecom providers need better data about gaps in service coverage and usage levels in order to make decisions about investments into new infrastructure buildouts for better connectivity options for rural communities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: broadband_data_2020October.csv

Acknowledgements

If you use this dataset in your research,...

Abstract

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

Dataset Citation

National Native Title Tribunal (2011) Registered and Notified Indigenous Land Use Agreements (ILUA) - agreement boundaries and core attributes about agreement - 01/11/2011. Bioregional Assessment Source Dataset. Viewed 05 July 2017, http://data.bioregionalassessments.gov.au/dataset/91df8ee7-423c-4a30-ae15-aa4c08a49bb9.

Public contracts with the City of Bloomington since 2018.

International agreements in force and applied for the purposes of exemption from customs duties or benefit from a reduced tariff

Local agreements with universities