Source documentation — Part 1. Final Report 1603-7.3./1993, main report 27 pp. and basic information160 pp., 17 source files (matches) with number. Inserts, 17 measuring stations master data sheets, 17 photos. — Unpublished. Study i.A. of the Kal National Park Association

This repository holds the Clarity Dataset which is a companion to the SANER'22 entitled "An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation". The dataset consists of 45,998 captions 10,204 GUI screenshots and xml metadata files (akin to the "html" for stipulating GUIs) of Android applications. The NL captions were obtained from human labelers, underwent several quality control mechanisms, and contain both high- (screen-level) and low-(component) level descriptions of screen functionality. This dataset is meant as a new source of data to augment techniques for software documentation that can take advantage of the rich pixel-based information contained within screenshots.

The text file "Air temperature.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2015. The primary source of the data is the Argonne National Laboratory, Illinois. The first four columns give year, month, day and hour of the observation. Column 5 is the data in degrees Fahrenheit. Column 6 is the three-digit data-source flag. They indicate if the air temperature data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. These flags consist of a three-digit sequence in the form "xyz". The user of the data should consult Over and others (2010) for the detailed documentation of this hourly data-source flag series. Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

This table contains the RXTE Mission-Long Source Catalog. It is generated from the RXTE Mission-Long Data Products, which are based on the standard data products (StdProds) from the PCA and HEXTE, for over 200 sources that have been observed many times with RXTE during its mission. Please refer to the RXTE GOF documentation for more information. This database table, first created in April 2008, contains the RXTE Mission-Long Source Catalog created by RXTE GOF. The catalog is updated by the RXTE GOF on a monthly basis; this HEASARC Browse table will be updated within one week of any updates made by the RXTE GOF. This is a service provided by NASA HEASARC .

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.

The text file "Wind speed.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2015. The primary source of the data is the Argonne National Laboratory, Illinois. The first four columns give year, month, day and hour of the observation. Column 5 is the data in miles per hour. Column 6 is the three-digit data-source flag to identify the wind speed data processing and they indicate if the data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. The data-source flag consist of a three-digit sequence in the form "xyz" that describe the origin and transformations of the data values. The user of the data should consult Over and others (2010) for the detailed documentation of this hourly data-source flag series. Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

govdocs1: source PDF files

[!NOTE] Converted versions of other document types (word, txt, etc) are available in this repo

This is ~220,000 open-access PDF documents (about 6.6M pages) from the dataset govdocs1. It wants to be OCR'd.

Uploaded as tar file pieces of ~10 GiB each due to size/file count limits with an index.csv covering details 5,000 randomly sampled PDFs are available unarchived in sample/. Hugging Face supports previewing these in-browser, for example this one… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/govdocs1-pdf-source.

The text file "Dewpoint temperature.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2015. The primary source of the data is the Argonne National Laboratory, Illinois. The first four columns give year, month, day and hour of the observation. Column 5 is the data in degrees Fahrenheit. Column 6 is the data-source flag consist of a three-digit sequence in the form "xyz". They indicate if the dewpoint temperature data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. The user of the data should consult Over and others (2010) for the detailed documentation of this hourly data-source flag series. Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

Abstract

Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are:

1.\tBioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx).

2.\tNaming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx).

3.\tData treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project Data Treatments v02.docx).

4.\tQuality Assurance of the Bioregional Assessment Impact and Risk Databases (IMIA Project Quality Assurance Protocol v17.docx).

This dataset also includes the Materialised View Information Manager (MatInfoManager.zip). This Microsoft Access database is used to manage the overlay definitions of materialized views of the Impact and Risk Analysis Databases. For more information about this tool, refer to the Data Treatments document.

The documentation supports all five Impact and Risk Analysis Databases developed for the assessment areas:

• Maranoa-Balonne-Condamine: http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a

• Gloucester: http://data.bioregionalassessments.gov.au/dataset/d78c474c-5177-42c2-873c-64c7fe2b178c

• Hunter: http://data.bioregionalassessments.gov.au/dataset/7c170d60-ff09-4982-bd89-dd3998a88a47

• Namoi: http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58

• Galilee: http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045​

Purpose

These documents describe end-to-end treatments of scientific data for the Impact and Risk Analysis Databases, developed and published by the Bioregional Assessment Programme. The applied approach to data quality assurance is also described. These documents are intended for people with an advanced knowledge in geospatial analysis and database administration, who seek to understand, restore or utilise the Analysis Databases and their underlying methods of analysis.

Dataset History

The Impact and Risk Analysis Database Documentation was created for and by the Information Modelling and Impact Assessment Project (IMIA Project).

Dataset Citation

Bioregional Assessment Programme (2018) Impact and Risk Analysis Database Documentation. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c.

The data and interpretations presented are based on firsthand experience, being compiled by the Department of Conservation and Land Management’s regional nature conservation staff between July 2001 and January 2002. Note: to access the data, select the data source link located on the right-hand side. Show full description

This archived SDOH Database (beta version) is available for reference. The most recent version of the SDOH Database replaces the beta version and is available on the main page. To ensure consistency in variable names and construction, analyses should not combine data from the beta version and the updated database.Download DataThe SDOH Data Source Documentation (PDF, 1.5 MB) file contains information for researchers about the structure and contents of the database and descriptions of each data source used to populate the database.The Variable Codebook (XLSX, 494 KB) Excel file provides descriptive statistics for each SDOH variable by year.***Microdata: YesLevel of Analysis: Local - Tract, CountyVariables Present: Separate DocumentFile Layout: .xslxCodebook: Yes Methods: YesWeights (with appropriate documentation): YesPublications: NoAggregate Data: Yes

A comparison table of popular database documentation tools, including supported DBMS, documentation formats, ease of use, customization options, and pricing.

Samples in this benchmark were generated by RELAI using the following data source(s): Data Source Name: flaml Documentation Data Source Link: https://microsoft.github.io/FLAML/docs/Getting-Started Data Source License: https://github.com/microsoft/FLAML?tab=MIT-1-ov-file#readme Data Source Authors: Observable AI Benchmarks by Data Agents © 2025 RELAI.AI. Licensed under CC BY 4.0. Source: https://relai.ai

Data organization and data mining represents one of the main challenges for modern high throughput technologies in pharmaceutical chemistry and medical chemistry. The presented open source documentation and analysis system provides an integrated solution (tutorial, setup protocol, sources, executables) aimed at substituting the traditionally used lab-book. The data management solution provided incorporates detailed information about the processing of the gels and the experimental conditions used and includes basic data analysis facilities which can be easily extended. The sample database and User-Interface are available free of charge under the GNU license from http://webber.physik.uni-freiburg.de/∼fallerd/tutorial.htm.

This document is included in the Gallatin Valley Sensitive Lands Protection Plan as Appendix C Part 1. It describes all data sources considered for the sensitive lands model that were not included in it's final version. Details for each data sources include layer name, the entity that created the data, year published, a brief explanation for its exclusion, and a link to the data download page. Note: The links in this document are not regularly maintained and may not be the most up to date access links. However, the other details provided for each data source should provide enough information to guide users to the correct download page.

This datasets was collected for a case study that sought to reconstruct the spatio-temporal spread of the American chestnut blight in Tennessee by collecting, reconciling, and analyzing heterogeneous archival and special collections materials from 1904 to 1950, the major period of infection and tree loss. Using the spread of the American chestnut blight in Tennessee as a case study, the project explored the potential for archival resources to serve as proxy data sources for the compilation of historical climate datasets.

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

What is in this release?

In this release you will find data about software distributed and/or crafted publicly on the Internet. You will find information about its development, its distribution and its relationship with other software included as a dependency. You will not find any information about the individuals who create and maintain these projects.

Further information and documentation on this data set can be found at https://libraries.io/data

For enquiries please contact data@libraries.io

This dataset contains seven csv files:

Projects

A project is a piece of software available on any one of the 34 package managers supported by Libraries.io.

Versions

A Libraries.io version is an immutable published version of a Project from a package manager. Not all package managers have a concept of publishing versions, often relying directly on tags/branches from a revision control tool.

Tags

A tag is equivalent to a tag in a revision control system. Tags are sometimes used instead of Versions where a package manager does not use the concept of versions. Tags are often semantic version numbers.

Dependencies

Dependencies describe the relationship between a project and the software it builds upon. Dependencies belong to Version. Each Version can have different sets of dependencies. Dependencies point at a specific Version or range of versions of other projects.

Repositories

A Libraries.io repository represents a publically accessible source code repository from either github.com, gitlab.com or bitbucket.org. Repositories are distinct from Projects, they are not distributed via a package manager and typically an application for end users rather than component to build upon.

Repository dependencies

A repository dependency is a dependency upon a Version from a package manager has been specified in a manifest file, either as a manually added dependency committed by a user or listed as a generated dependency listed in a lockfile that has been automatically generated by a package manager and committed.

Projects with related Repository fields

This is an alternative projects export that denormalizes a projects related source code repository inline to reduce the need to join between two data sets.

Licence

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International Licence.

This licence provides the user with the freedom to use, adapt and redistribute this data. In return the user must publish any derivative work under a similarly open licence, attributing Libraries.io as a data source. The full text of the licence is included in the data.

Access, Attribution and Citation

The dataset is available to download from Zenodo at https://zenodo.org/record/2536573.

Please attribute Libraries.io as a data source by including the words ‘Includes data from Libraries.io, a project from Tidelift’ and reference the Digital Object identifier: 10.5281/zenodo.3626071

This is the source code package for the labbench python module, version 0.20, which is its first public release. The purpose of labbench is to streamline and organize complicated laboratory automation tasks that involve large-scale benchtop automation, concurrency, and/or data management. It is built around a system of wrappers that facilitate robust, concise exception handling, type checking, API conventions, and synchronized device connection through python context blocks. The wrappers also provide convenient new functionality, such as support for automated status displays in jupyter notebooks, simplified threaded concurrency, and automated, type-safe logging to relational databases. Together, these features help to minimize the amount of "copy-and-paste" code that can make your lab automation scripts error-prone and difficult to maintain. The python code that results can be clear, concise, reusable and maintainable, and provide consistent formatting for stored data. The result helps researchers to meet NIST's open data obligations, even for complicated, large, and heterogeneous datasets. Several past and ongoing projects in the NIST Communication Technology Laboratory (CTL) published data that were acquired by automation in labbench. We release it here both for transparency and to invite public use and feedback. Ongoing updates to this source code will be maintained on the NIST github page at https://github.com/usnistgov/labbench. The code was developed in python, documented with the python sphinx package and markdown, and shared through the USNISTGOV organization on GitHub. INSTALLATION labbench can run on any computer that supports python 3.6. The hardware requirements are discussed here: https://docs.anaconda.com/anaconda/install/#requirements 1. Install your favorite distribution of a python version 3.6 or greater 2. In a command prompt, pip install git+https://gitlab.nist.gov/gitlab/ssm/labbench 3. (Optional) install an NI VISA [1] runtime, for example this one for windows. USAGE The source distribution contains detailed information including * README.md - documentation to get started using labbench * LICENSE.md - license and redistribution information * doc/labbench-api.pdf - complete listing of the module and documentation