During the COVID-19 pandemic, many public schools across the United States shifted from fully in-person learning to alternative learning modalities such as hybrid and fully remote learning. In this study, data from 14,688 unique school districts from August 2020 to June 2021 were collected to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. These data were provided by Burbio, MCH Strategic Data, the American Enterprise Institute’s Return to Learn Tracker and individual state dashboards. Because the modalities reported by these sources were incomplete and occasionally misaligned, a model was needed to combine and deconflict these data to provide a more comprehensive description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in order to obtain more reliable data in support of public health surveillance and research efforts.

Proportions of participants identified as HCV seropositive according to each individual data source and all three sources combined, among cohort and among those tested.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

The Public Health Emergency (PHE) declaration for COVID-19 expired on May 11, 2023. As a result, the Aggregate Case and Death Surveillance System will be discontinued. Although these data will continue to be publicly available, this dataset will no longer be updated.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily.

This dataset includes the URLs that were used by the aggregate county data collection process that compiled aggregate case and death counts by county. Within this file, each of the states (plus select jurisdictions and territories) are listed along with the county web sources which were used for pulling these numbers. Some states had a single statewide source for collecting the county data, while other states and local health jurisdictions may have had standalone sources for individual counties. In the cases where both local and state web sources were listed, a composite approach was taken so that the maximum value reported for a location from either source was used. The initial raw data were sourced from these links and ingested into the CDC aggregate county dataset before being published on the COVID Data Tracker.

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigators if further information is needed. The current study subjected the body of empirical literature on school violence to a meta-analysis or "quantitative synthesis", to determine the key individual-, school-, and community-level factors that influence violence in school. The data are based on 693 studies of school violence that contributed a total of 8,551 effect size estimates--3,840 for delinquency/aggression (44.91%) and 4,711 for victimization (55.09%). These effect sizes were drawn from 545 independent data sets and 68 different countries. The majority of effect size estimates (56.22%) were based on U.S. samples. A total of 31 different predictors of school violence were coded at the individual, institutional, and community levels. The collection includes one Stata file, Meta-Analysis-Data-for-NACJD.dta (n=8,551; 9 variables).

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask:

A. Guiding Questions:
1. Who are the key stakeholders and what are their goals for the data analysis project? 2. What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: 1. Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team.

1. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare:

A. Guiding Questions: 1. Where is the data stored and organized? 2. Are there any problems with the data? 3. How does the data help answer the business question?

B. Key Tasks:

1. Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016.
   *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDay_merged.csv -dailyActivity_merged.csv

2. Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual ...

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Individual recipients may connect to more than one outcome measure. This table maps the individual recipient's ID (from the Individual Recipients - Rescue Plan Data Standards dataset) to Outcome Type ID (from the Individual Recipient Outcomes dataset). This dataset will populate as projects move further into implementation.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=60976

These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.

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

China Asset Mgt Business: Fund Co.: Source of Fund: Individual data was reported at 4.350 % in 2022. This records a decrease from the previous number of 4.510 % for 2021. China Asset Mgt Business: Fund Co.: Source of Fund: Individual data is updated yearly, averaging 4.455 % from Dec 2014 (Median) to 2022, with 6 observations. The data reached an all-time high of 10.000 % in 2014 and a record low of 3.750 % in 2020. China Asset Mgt Business: Fund Co.: Source of Fund: Individual data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Source of Fund.

Information related to diet and energy flow is fundamental to a diverse range of Antarctic and Southern Ocean biological and ecosystem studies. This metadata record describes a database of such information being collated by the SCAR Expert Groups on Antarctic Biodiversity Informatics (EG-ABI) and Birds and Marine Mammals (EG-BAMM) to assist the scientific community in this work. It includes data related to diet and energy flow from conventional (e.g. gut content) and modern (e.g. molecular) studies, stable isotopes, fatty acids, and energetic content. It is a product of the SCAR community and open for all to participate in and use.

Data have been drawn from published literature, existing trophic data collections, and unpublished data. The database comprises five principal tables, relating to (i) direct sampling methods of dietary assessment (e.g. gut, scat, and bolus content analyses, stomach flushing, and observed predation), (ii) stable isotopes, (iii) lipids, (iv) DNA-based diet assessment, and (v) energetics values. The schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data.

A range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source.

Notes on names: Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/). For uncertain taxa, the most specific taxonomic name has been used (e.g. prey reported in a study as "Pachyptila sp." will appear here as "Pachyptila"; "Cephalopods" will appear as "Cephalopoda"). Uncertain species identifications (e.g. "Notothenia rossii?" or "Gymnoscopelus cf. piabilis") have been assigned the genus name (e.g. "Notothenia", "Gymnoscopelus"). Original names have been retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) are given where possible.

Grouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The PREY_IS_AGGREGATE column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with "N" in the PREY_IS_AGGREGATE column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with "Y" in the PREY_IS_AGGREGATE column. See the notes on PREY_IS_AGGREGATE, below.

There are two related R packages that provide data access and functionality for working with these data. See the package home pages for more information: https://github.com/SCAR/sohungry and https://github.com/SCAR/solong.

Data table schemas

Sources data table

• SOURCE_ID: The unique identifier of this source

• DETAILS: The bibliographic details for this source (e.g. "Hindell M (1988) The diet of the royal penguin Eudyptes schlegeli at Macquarie Island. Emu 88:219–226")

• NOTES: Relevant notes about this source – if it’s a published paper, this is probably the abstract

• DOI: The DOI of the source (paper or dataset), in the form "10.xxxx/yyyy"

Diet data table

• RECORD_ID: The unique identifier of this record

• SOURCE_ID: The identifier of the source study from which this record was obtained (see corresponding entry in the sources data table)

• SOURCE_DETAILS, SOURCE_DOI: The details and DOI of the source, copied from the sources data table for convenience

• ORIGINAL_RECORD_ID: The identifier of this data record in its original source, if it had one

• LOCATION: The name of the location at which the data was collected

• WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values for western hemisphere longitudes)

• EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values for western hemisphere longitudes)

• SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values for southern hemisphere latitudes)

• NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values for southern hemisphere latitudes)

• ALTITUDE_MIN: The minimum altitude of the sampling region, in metres

• ALTITUDE_MAX: The maximum altitude of the sampling region, in metres

• DEPTH_MIN: The shallowest depth of the sampling, in metres

• DEPTH_MAX: The deepest depth of the sampling, in metres

• OBSERVATION_DATE_START: The start of the sampling period

• OBSERVATION_DATE_END: The end of the sampling period. If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), this will be the first and last dates (in this example, from 1-Jan-2002 to 31-Jan-2003)

• PREDATOR_NAME: The name of the predator. This may differ from predator_name_original if, for example, taxonomy has changed since the original publication, if the original publication had spelling errors or used common (not scientific) names

• PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source

• PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register

• PREDATOR_WORMS_RANK, PREDATOR_WORMS_KINGDOM, PREDATOR_WORMS_PHYLUM, PREDATOR_WORMS_CLASS, PREDATOR_WORMS_ORDER, PREDATOR_WORMS_FAMILY, PREDATOR_WORMS_GENUS: The taxonomic details of the predator, from the WoRMS taxonomic register

• PREDATOR_GROUP_SOKI: A descriptive label of the group to which the predator belongs (currently used in the Southern Ocean Knowledge and Information wiki, http://soki.aq)

• PREDATOR_LIFE_STAGE: Life stage of the predator, e.g. "adult", "chick", "larva", "juvenile". Note that if a food sample was taken from an adult animal, but that food was destined for a juvenile, then the life stage will be "juvenile" (this is common with seabirds feeding chicks)

• PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable, e.g. "brooding", "chick rearing", "nonbreeding", "posthatching"

• PREDATOR_SEX: Sex of the predator: "male", "female", "both", or "unknown"

• PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20. For scat content studies, this will be the number of scats analysed

• PREDATOR_SAMPLE_ID: The identifier of the predator(s). If predators are being reported at the individual level (i.e. PREDATOR_SAMPLE_COUNT = 1) then PREDATOR_SAMPLE_ID is the individual animal ID. Alternatively, if the data values being entered here are from a group of predators, then the PREDATOR_SAMPLE_ID identifies that group of predators. PREDATOR_SAMPLE_ID values are unique within a source (i.e. SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or group of individuals, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will sometimes report detailed prey information for a large sample, but then report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season). In the simplest case, the diet of each predator will be reported only once in the study, and in this scenario the PREDATOR_SAMPLE_ID values will simply be 1 to N (for N predators).

• PREDATOR_SIZE_MIN, PREDATOR_SIZE_MAX, PREDATOR_SIZE_MEAN, PREDATOR_SIZE_SD: The minimum, maximum, mean, and standard deviation of the size of the predators in the sample

• PREDATOR_SIZE_UNITS: The units of size (e.g. "mm")

• PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. "total length", "standard length")

• PREDATOR_MASS_MIN, PREDATOR_MASS_MAX, PREDATOR_MASS_MEAN, PREDATOR_MASS_SD: The minimum, maximum, mean, and standard deviation of the mass of the predators in the sample

• PREDATOR_MASS_UNITS: The units of mass (e.g. "g", "kg")

• PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents

• PREY_NAME: The scientific name of the prey item (corrected, if necessary)

• PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source

PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register

• PREY_WORMS_RANK, PREY_WORMS_KINGDOM, PREY_WORMS_PHYLUM, PREY_WORMS_CLASS, PREY_WORMS_ORDER, PREY_WORMS_FAMILY, PREY_WORMS_GENUS: The taxonomic details of the prey, from the WoRMS taxonomic register

• PREY_GROUP_SOKI: A descriptive label of the group to which the prey belongs (currently used in the Southern Ocean Knowledge and Information wiki, http://soki.aq)

• PREY_IS_AGGREGATE: "Y" indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses

• PREY_LIFE_STAGE: Life stage of the prey (e.g. "adult", "chick", "larva")

• PREY_SEX: The sex of the prey ("male", "female", "both", or "unknown"). Note that this is generally "unknown"

• PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: this is NOT the total number of individuals of

Comprehensive dataset containing 39 verified Single Source locations in United States with complete contact information, ratings, reviews, and location data.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Shoreline Construction Lines Dataset

Mapping of Shoreline Construction Lines

By Homeland Infrastructure Foundation [source]

About this dataset

Within this dataset, users can find numerous attributes that provide insight into various aspects of shoreline construction lines. The Category_o field categorizes these structures based on certain characteristics or purposes they serve. Additionally, each object in the dataset possesses a unique name or identifier represented by the Object_Nam column.

Another crucial piece of information captured in this dataset is the status of each shoreline construction line. The Status field indicates whether a particular structure is currently active or inactive. This helps users understand if it still serves its intended purpose or has been decommissioned.

Furthermore, the dataset includes data pertaining to multiple water levels associated with different shoreline construction lines. This information can be found in the Water_Leve column and provides relevant context for understanding how these artificial coastlines interact with various water bodies.

To aid cartographic representations and proper utilization of this data source for mapping purposes at different scales, there is also an attribute called Scale_Mini. This value denotes the minimum scale necessary to visualize a specific shoreline construction line accurately.

Data sources are important for reproducibility and quality assurance purposes in any GIS analysis project; hence identifying who provided and contributed to collecting this data can be critical in assessing its reliability. In this regard, individuals or organizations responsible for providing source data are specified in the column labeled Source_Ind.

Accompanying descriptive information about each source used to create these shoreline constructions lines can be found in the Source_D_1 field. This supplemental information provides additional context and details about the data's origin or collection methodology.

The dataset also includes a numerical attribute called SHAPE_Leng, representing the length of each shoreline construction line. This information complements the geographic and spatial attributes associated with these structures.

How to use the dataset

• Understanding the Categories:

  • The Category_o column classifies each shoreline construction line into different categories. This can range from seawalls and breakwaters to jetties and groins.
  • Use this information to identify specific types of shoreline constructions based on your analysis needs.

• Identifying Specific Objects:

  • The Object_Nam column provides unique names or identifiers for each shoreline construction line.
  • These identifiers help differentiate between different segments of construction lines in a region.

• Determining Status:

  • The Status column indicates whether a shoreline construction line is active or inactive.
  • Active constructions are still in use and may be actively maintained or monitored.
  • Inactive constructions are no longer operational or may have been demolished.

• Analyzing Water Levels:

  • The Water_Leve column describes the water level at which each shoreline construction line is located.
  • Different levels may impact the suitability or effectiveness of these structures based on tidal changes or flood zones.

• Exploring Additional Information:

  • The Informatio column contains additional details about each shoreline construction line.
  • This can include various attributes such as materials used, design specifications, ownership details, etc.

• Determining Minimum Visible Scale:
  -- The Scale_Mini column specifies the minimum scale at which you can observe the coastline's man-made structures clearly.

• Verifying Data Sources: -- In order to understand data reliability and credibility for further analysis,Source_Ind, Source_D_1, SHAPE_Leng,and Source_Dat columns provide information about the individual or organization that provided the source data and length, and date of the source data used to create the shoreline construction lines.

Utilize this dataset to perform various analyses related to shorelines, coastal developments, navigational channels, and impacts of man-made structures on marine ecosystems. The combination of categories, object names, status, water levels, additional information, minimum visible scale and reliable source information offers a comprehensive understanding of shoreline constructions across different regions.

Remember to refer back to the dataset documentation for any specific deta...

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

The WISE 3-Band Cryo Single Exposure (L1b) Source Table contains positions and photometry in the 3.4, 4.6 and 12 μm bands for 3,703,319,374 sources extracted from observations made during the WISE 3-Band Cryo survey phase, 6 August 2010 through 29 September 2010. WISE scanned approximately 30% of the sky during this period when the telescope and focal planes operated at a slightly higher temperature, but were still cooled by solid hydrogen in the inner cryogen tank.

BackgroundEstimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.MethodsBaseline survey data for 90,352 participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and over—were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (κ).The characteristics of people classified as multimorbid were compared using logistic regression modelling.ResultsAgreement was found to be highest for diabetes in both hospital and claims data (κ = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.ConclusionsDifferent individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.