In Ontario, there is a movement afoot to mark up surveys in DDI and put them in an interface that allows them to be shared with other universities. A noble exercise, indeed! Our project, (Ontario Data Documentation, Extraction Service and Infrastructure Initiative) provides university researchers with unprecedented access to a significant number of datasets in a web-based data extraction system. Access to the data with its accompanying standardized metadata is key to our project. However, the staff marking up these surveys do not necessarily think alike, so the formats used in marking up the surveys can and do vary across institutions. And this is taking place in only one province so this begs the question of what the formatting looks like when the marking up is done nationally. In this presentation, we will discuss the five Ws of a Best Practices Document: why we need one; when it happened; where it was put together; what the process was; and who will benefit from it.

Resources for GDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the GDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting GDR metadata for federation or inclusion in their local catalogs.

Resources for MHKDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the MHKDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting MHKDR metadata for federation or inclusion in their local catalogs.

Resources for Water DAMS data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of Water DAMS. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting Water DAMS metadata for federation or inclusion in their local catalogs.

Spreadsheet with data about whether or not the indicated institutional repository website provides metadata documentation. See readme file for more information.

Resources for OEDI data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of OEDI. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting OEDI metadata for federation or inclusion in their local catalogs.

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

Dataset from the Online Survey of the Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group.

The data was collected from November 8, 2021 to January 14, 2022.

The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide."

• Demographics
• Data Description and Collection
• Data Documentation & Quality
• Data Archiving, Publishing & Sharing After the Project
• Guidelines, Principles & Best Practices
• Follow-up interviews and group discussions

The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication.

In addition to the data, the variables and values are also published here.

The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373

A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).

You will be using tutorials (developed at the University of Guelph and Queen's University) and giving feedback on what you liked, what you found difficult, and further directions for the futher development of more-advanced tutorials.

Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

New York State's focus on data quality has been a hallmark of Open NY. This document is intended to be read together with the NYS Open Data Handbook, (https://data.ny.gov/dataset/NYS-Open-Data-Handbook/id8k-natf), and includes best practices garnered from lessons learned regarding optimal formatting and documentation. This guide represents a commitment to continuous quality improvement to maximize understanding, and the advancement of standardization to promote interoperability, analysis, and utilization of the data.

The City of Detroit Open Data Style Guide details standards that, when implemented, improve the public understandability and accessibility of the City's open data. The Style Guide is broken up into two sections. The dataset section outlines best practices for data formatting, quality, and accessibility. The metadata section provides guidance on creating rich and informative dataset descriptions, column-level descriptions, and more. Eventually, all items on the Open Data Portal will adhere to the Style Guide.

The dataset contains tabular information on the elements of best practice in scholarly publishing found in a set of documents (high-level recommendations and principles, indexation criteria and specific assessment guidelines used on the national and institutional levels). The set of documents subject to analysis (58 items) were identified by the DIAMAS project team members (bibliographic metadata are provided in IPSP-best-practice-documents.xml and IPSP-best-practice-documents.ris).

The dataset was compiled by the DIAMAS project team using an analysis matrix that included the general information about the documents (title, issuing entity, scope and purpose, etc.) and the the seven core components of scholarly publishing identified in the Diamond Open Access Action Plan (2022) and revised by the DIAMAS project team.

More information about the data collection methodology can be found in the report D3.1 IPSP Best Practices Quality evaluation criteria, best practices, and assessment systems for Institutional Publishing Service Providers (IPSPs) (https://doi.org/10.5281/zenodo.7859172), which is based on this dataset.

****Dataset contents****

IPSPs_best-practices-overview.csv

IPSPs_best-practices-overview.ods

IPSP-best-practice-documents.xml

IPSP-best-practice-documents.ris

README.txt

****Column headers and field types***

Title (original) (text)

Title (English) (text)

Publication date (date, DD/MM/YY)

Last accessed (date, DD/MM/YY)

URL (text-web address)

Scope (text, controlled)

Type of document (text, controlled)

Original language (text)

Other languages (text)

Entity issuing the document (text)

Entity responsible for the assessment (text)

Scope of the assessment (text, controlled)

Scope of assessment: region or country (text)

Disciplines’ coverage (text)

Periodicity of the assessment (text)

Reassessment frequency? If yes: periodicity (text)

Benefits linked to the assessment (text)

(1) Funding (text)

(2) Ownership and governance (text)

(3) Open science practices (text)

(4) Editorial quality, editorial management and research integrity (text)

(5) Technical service efficiency (text)

(6) Visibility (including indexation), communication, marketing and impact (text)

(7) Diversity, Equity and Inclusion (text)

The NC OneMap Geoportal has matured into an essential resource many professionals rely upon to perform their daily business. Data managers and custodians are urged to adopt the recommendations contained in this document. Doing so will ensure that NC OneMap continues to be an easy-to-use and reliable data discovery tool providing resources that have consistent capabilities and are well-documented. This document provides guidance for data managers to configure geospatial resources in uniform, consistent ways for successful discovery within NC OneMap.

Homework, midterm and final exam assignments for a series of lectures from a 10-week, 2-credit graduate-level course in research data management.The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines." Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation. The course was offered for the first time during the Winter 2014 term (January - March). See the associated syllabus, lesson plans and lectures.

📚 AWS PDF Chunk Dataset

This dataset consists of chunked text extracted from all publicly available PDF documents on the Amazon Web Services (AWS) official website. The data includes whitepapers, user guides, technical documentation, and best practice manuals—covering virtually every AWS service, concept, and architecture in depth. It is designed to serve as a high-quality knowledge base for use in embedding generation, vector databases, and retrieval-augmented generation (RAG)… See the full description on the dataset page: https://huggingface.co/datasets/semihk1/aws-public-pdf-chunked-dataset.

Mosquito surveillance data, obtained through various trapping methods, are compiled and shared in Excel (.xlsx) files. The E4Warning dataset template aims to assist field researchers in their data archiving efforts by aligning with the project's Data Management Plan. It consists of two main components: metadata and data. The metadata component includes information about the origin of the dataset, such as study details and licensing for usage. This ensures that all necessary contextual information is accessible. Our metadata component utilizes the template generated by MIReAD (Minimum Information for Reusable Analytical Data) to ensure high standards of data documentation and reusability of arthropod abundance data by establishing a set of guidelines for data reporting. By adopting the MIReAD template for our metadata, we align our data management practices with best practices for data standardization and transparency The data component lists and describes the specific data fields that should be included in data collection sheets. This is tailored to capture the essential variables typically collected by academic researchers and surveillance initiatives. The template serves as a comprehensive checklist to help prevent the omission of crucial information. The mosquito surveillance data template utilized by E4Warning partners is a designed document for recording data from mosquito trapping activities, which is subsequently used for modeling. Each field within the template is structured to ensure a comprehensive understanding of the surveillance efforts and the possible biases introduced by the trapping devices and attractants used. [1] Rund et al. 2019. MIReAD, a minimum information standard for reporting arthropod abundance data. Scientific Data. 6: 40.

The Document Drafting Solutions Software market has emerged as a pivotal segment within the broader technology landscape, enabling businesses and professionals to streamline their documentation processes. This software is designed to assist users in creating, managing, and finalizing various types of documents with

The Document Management and Retrieval System (DMRS) market has emerged as an essential component in the digital transformation landscape, catering to businesses seeking efficient ways to handle, store, and access documents. These systems facilitate the digitization of paper-based documents, promoting streamlined pro