In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

Open Data Handbook Curation Detailed Diagram. DC’s data submission process involves four steps as depicted in this diagram. Overall, the data analysts guide the data owner and other analysts as needed to run the data through the submission process, with different groups leading each part (see all caps in diagram above). Their application occurs in a unified database infrastructure consisting of a data warehouse and geospatial database. While there are specific processes and guidelines for each database, they share an overall setting where consistency and standardization are promoted and supported for their individual data curation processes.

List of data curation process description rationale.

The purpose of the online survey on data curation was to arrive at a better understanding of the process of creating, organizing, and maintaining data(sets) by organizations in the field of grey literature. The survey population was based on the number of respondents to the earlier questionnaire on Data Retention Status, which was the first phase in the study on global information repository research for STI development. The ten-question online survey was constructed and implemented via SurveyMonkey. Nine of the questions required closed-ended checkbox responses, while the tenth was open-ended. The closed-ended part of the questionnaire dealt with such issues as the strengths and tasks of the organization related to data curation, improving the user experience, collaboration on data sharing, and the introduction of AI technology in the work environment. The results of the survey remain compiled and preserved in SurveyMonkey as well as in DANS, Data Station for the Social Sciences and Humanities.

List of properties used in data curation process ontology.

Wavespeech was a site-specific collaborative project curated by Mike Tooby at the Pier Arts Centre, Stromness, Orkney in 2015. In collaboration with the artists Edmund De Wall and David Ward, the in-house gallery curatorial team, poet and writer Rhona Warwick Paterson and local volunteers.This item contains an outline document for the Wavespeech exhibition, and serves as contextualisation for the project.The work is under copyright and may not be used without permission. Use of this repository acknowledges cooperation with its policies and relevant copyright law.

Distribution count and response rate for the data curation network’s “data curation service survey”.

Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publically available PHYSPROP physico-chemical properties and environmental fate datasets. The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name, CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers, and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only the highest quality subset of the original dataset was compared to the larger curated and corrected data set. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publically available for further usage and integration by the scientific community. This dataset is associated with the following publication: Mansouri, K., C. Grulke, A. Richard, R. Judson, and A. Williams. (SAR AND QSAR IN ENVIRONMENTAL RESEARCH) An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modeling. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(11): 911-937, (2016).

Content Curation Software Market Outlook

The global Content Curation Software market size is projected to grow from USD 1.2 billion in 2023 to USD 3.8 billion by 2032, at a compound annual growth rate (CAGR) of 13.7% over the forecast period. This significant growth can be attributed to increasing demand for personalized content, advancements in artificial intelligence (AI) and machine learning, and the growing importance of content marketing strategies among businesses.

The rising demand for personalized content is a major growth factor for the Content Curation Software market. In an era where consumers are bombarded with information from multiple sources, personalized content helps in capturing their attention and retaining their interest. Businesses are increasingly leveraging content curation software to deliver customized content that resonates with their target audience. This trend is particularly pronounced in sectors such as retail and e-commerce, where personalized product recommendations can significantly impact sales and customer loyalty.

Advancements in AI and machine learning technologies are also driving the growth of the Content Curation Software market. These technologies enable more effective data analysis and content recommendations, thereby enhancing the user experience. AI-powered content curation tools can analyze vast amounts of data to identify trends, preferences, and behaviors, which in turn allows for more accurate content suggestions. This capability is particularly valuable for social media management and content discovery applications, where timely and relevant content is crucial.

Another key growth driver is the increasing emphasis on content marketing strategies among businesses. Content marketing has proven to be an effective way to engage customers, build brand awareness, and drive conversions. As a result, companies are investing in content curation software to streamline their content marketing efforts. These tools help in aggregating and sharing high-quality content from various sources, thereby saving time and resources. The integration of content curation software with other marketing tools further amplifies its benefits, making it a critical component of modern marketing strategies.

The evolution of Content Curation Software is deeply intertwined with the broader landscape of digital content. As businesses strive to maintain a competitive edge, the ability to manage and optimize their Content has become paramount. This software not only aids in organizing and presenting information but also plays a crucial role in enhancing user engagement and driving brand loyalty. By leveraging sophisticated algorithms and analytics, content curation tools provide insights that help businesses tailor their strategies to meet the ever-changing demands of their audience. The integration of these tools into existing workflows ensures that content remains relevant, timely, and impactful.

From a regional perspective, North America dominates the Content Curation Software market, owing to the high adoption of advanced technologies and the presence of major market players. Europe is also a significant market, driven by the increasing focus on digital marketing and content personalization. The Asia Pacific region is expected to witness the highest growth during the forecast period, fueled by the rapid digitalization of businesses and the growing popularity of social media platforms. Latin America and the Middle East & Africa are emerging markets, with increasing investments in digital infrastructure and marketing technologies.

Component Analysis

The Content Curation Software market is segmented by components into software and services. The software component dominates the market, driven by the increasing adoption of advanced content curation tools. These software solutions are designed to automate the process of discovering, aggregating, and sharing content, making it easier for businesses to manage their content marketing efforts. Features such as AI-powered content recommendations, analytics, and integration capabilities with other marketing tools make these software solutions highly valuable for businesses of all sizes.

The services segment, although smaller in comparison to the software segment, plays a critical role in the overall market. Services include consulting, implementation, training, and support, which are essential for the successful deployment and utilization of content curati

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields. However, not only data sharing within research fields but also interdisciplinary data sharing is required to promote open science. For this purpose, knowledge of data curation across the research fields is surveyed, analyzed, and organized as an ontology in this paper. As the survey, existing vocabularies and procedures are collected and compared as well as interviews with the data curators in research institutes in different fields are conducted to clarify commonalities and differences in data curation across the research fields. It turned out that the granularity of tasks and procedures that constitute the building blocks of data curation is not formalized. Without a method to overcome this gap, it will be challenging to promote interdisciplinary reuse of research data. Based on the analysis above, the ontology for the data curation process is proposed to describe data curation processes in different fields universally. It is described by OWL and shown as valid and consistent from the logical viewpoint. The ontology successfully represents data curation activities as the processes in the different fields acquired by the interviews. It is also helpful to identify the functions of the systems to support the data curation process. This study contributes to building a knowledge framework for an interdisciplinary understanding of data curation activities in different fields.

Identifiers of many kinds are the key to creating unambiguous and persistent connections between research objects and other items in the global research infrastructure (GRI). Many repositories are implementing mechanisms to collect and integrate these identifiers into their submission and record curation processes. This bodes well for a well-connected future, but many existing resources submitted in the past are missing these identifiers, thus missing the connections required for inclusion in the connected infrastructure. Re-curation of these metadata is required to make these connections. The Dryad Data Repository has existed since 2008 and has successfully re-curated the repository metadata several times, adding identifiers for research organizations, funders, and researchers. Understanding and quantifying these successes depends on measuring repository and identifier connectivity. Metrics are described and applied to the entire repository here. Identifiers for papers (DOIs) connected..., These data are Dryad metadata retrieved from https://datadryad.org and translated into csv files. There are two datasets: Â 1. DryadJournalDataset was retrieved from Dryad using the ISSNs in the file DryadJournalDataset_ISSNs.txt, although some had no data. Â 2. DryadOrganizationDataset was retrieved from Dryad using the RORs in the file DryadOrganizationDataset_RORs.txt, although some had no data. Each dataset includes four types of metadata: identifiers, funders, keywords, and related works, each in a separate comma (.csv) or tab (.tsv) delimited files. There are also Microsoft Excel files (.xlsx) for the identifier metadata and connectivity summaries for each dataset (*.html). The connectivity summaries include summaries of each parameter in all four data files with definitions, counts, unique counts, most frequent values, and completeness. These data formed the basis for an analysis of the connectivity of the Dryad repository for organizations, funders, and people., , # Data For: Sustainable Connectivity in a Community Repository

GENERAL INFORMATION

This readme.txt file was generated on 30231110 by Ted Habermann

Title of Dataset

Data For: Sustainable Connectivity in a Community Repository

Author Information

Principal Investigator Contact Information Name: Ted Habermann (0000-0003-3585-6733) Institution: Metadata Game Changers () Email: ORCID: 0000-0003-3585-6733

Date published or finalized for release:

November 10, 2023

Date of data collection (single date, range, approximate date)

May and June 2023

Information about funding sources that supported the collection of the data:

National Science Foundation (Crossref Funder ID: 100000001) Award 2134956.

Overview of the data (abstract):

These data are Dryad metadata retrieved from and translated into csv files. There are two datasets:

1. DryadJournalDataset was retrieved from Dryad using the ISSNs in the file DryadJournalDataset_ISSNs.txt, although some had n...

This dataset includes the evaluation of The New York Times newsletters conducted in March 2023. The analysis applies the Curation Analysis System (CAS) methodology, developed by Guallar et al. (2021b), which provides a structured framework for assessing curated journalistic content. The dataset is part of the project "Parameters and Strategies to Increase the Relevance of Media and Digital Communication in Society: Curation, Visualisation, and Visibility (CUVICOM)" (PID2021-123579OB-I00), funded by the Ministry of Science and Innovation.

Files Included: FinalScores.xlsx Description: This file contains the scoring results for The New York Times newsletters. The file includes multiple tabs that organise the data into: Overall Scores: Aggregated scores assigned to each newsletter based on the evaluation criteria. Scores by Section: Breakdown of scores by specific sections or categories of newsletters. Rankings by Item: Rankings derived from individual parameters in the scoring rubric. Purpose: To provide a quantitative assessment of newsletter performance using the CAS methodology. CodingSheet_CAS_Methodology.pdf

Description: This document details the Curation Analysis System (CAS) method. It outlines the two primary dimensions of analysis: Content Dimension: Evaluates aspects such as the quantity of curated content, its time range (e.g., retrospective or real-time), origin (internal or external), and source characteristics (type and format). Curation Dimension: Focuses on curatorial processes, including authorship visibility, techniques like summarising or quoting, and the journalistic purpose of links (e.g., informing or contextualising). Purpose: To guide the evaluation process by defining the parameters and procedures used in scoring and analysis.

The study uses the data curation profiling (DCP) method to capture all actions across the data lifecycle. Participants responded to these open-ended questions that relate to aspects of the data lifecycle–the accession, organization, storage, and use of their collections. The purpose of this study is to understand the curation perceptions and behaviors of physical collection managers across domains to inform cross-disciplinary research data management. Ten focus groups were conducted with thirty-two participants across several physical collection communities. Participants responded to open-ended questions that relate to the entire data lifecycle for their physical objects. Results indicated that physical collections attempt to use universal metadata and data storage standards to increase discoverability, but interdisciplinary physical collections and derived data reuse require more investments to increase reusability of these invaluable items. This study concludes with a domain-agnostic reuse facets matrix to inform investment in cyberinfrastructure tools and services.

The Institute of Classical Archaeology and Texas Advanced Computing Center have developed the distributed curation model illustrated in these graphics, associated with the poster presented at the poster session entitled The Afterlife of Archaeological Information: Use and Reuse of Digital Archaeological Data at the SAA 80th Annual Meeting. The "collection architecture" presented here integrates existing cyberinfrastructure resources at the University of Texas at Austin, along with an automated metadata platform and procedures to organize and prepare the ICA collection for preservation and sharing, without interrupting ongoing work. The system streamlines process of data publication and archiving as the collection is organized, shared, documented, and analyzed "on-the-fly" during study and publication activities. All of these tasks are performed in parallel within a unified architecture by a multidisciplinary team located in the USA and abroad.

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Archaeologists generate large numbers of digital materials during the course of field, laboratory, and records investigations. Maps, photographs, data analysis, and reports are often produced digitally. Good curation of digital data means it can be discovered and accessed, and preserving these materials means they are accessible for future use. In many ways the managing, curating and preserving digital materials involves similar steps as those taken with physical artifacts, samples, and paper records. However, the digital materials are different and the process can appear daunting at first.

In this poster we outline some simple steps for managing and curating digital materials that can be integrated into existing or future project and that can be applied to digital materials from completed projects. We will also use real world examples from tDAR (the Digital Archaeological Record) to illustrate how people are preserving their digital materials for access and future use.

Results of input–process-output analysis of data curation activity vocabularies.

Social media transition into algorithmic content recommendations has reshaped platforms’ consumptive curation affordances, reducing users’ explicit curation tools. In this article I interrogate the role of platforms’ interface design in shaping these new affordances. Building on existing literature on digital curation, social media affordances and mediation theory, I present affordances as relational, shaped by the interplay between social media technology, user practices, and social arrangements. Platforms’ software interface acts as a mediator between consumptive curation practices and algorithmic recommendations, establishing an initial environment that attempts to shape user practices. To illustrate this, I analyse TikTok’s interface through a walkthrough method, organised according to the algorithmic experience framework. Findings show the dominant role of the For You Page, how TikTok encourages passive consumption through watching and scrolling and how it discourages disabling data sources, among others. These insights serve as a foundation for future user-centred studies on how users react to and reappropriate TikTok’s interface to fulfil their consumptive curation goals.

Instructions and guidance materials on how to prepare your research data for sharing and long-term access and how to deposit your research data in the University of Guelph Research Data Repositories (Data Repositories).How the Data Repositories work:Self-deposit with mediation data deposit service: Upon request, depositors are given dataset creator access to a collection in the Data Repositories allowing them to create new draft dataset records and submit their draft datasets for review. Repository staff review all submitted datasets for alignment with repository policies and data deposit guidelines. Repository staff will work with depositors to make any required changes to the metadata, data files, and/or supplemental documentation to improve the FAIRness (findability, accessibility, interoperability, and reusability) of the dataset. When the dataset is ready, repository staff will make the dataset publicly available in the repository on behalf of the depositor. How to start the deposit process: If you are interested in depositing data in the Data Repositories and/or have questions about preparing your data for deposit, please contact repository staff to start the process.

Data relating to the publication. Sharing research data and scholarship is of national importance because of the increased focus on maximizing return on the U.S. government's investment in research programs. Recent government policy changes have directly affected the management and accessibility of publically funded research. On January 18, 2011, the National Science Foundation, a U.S. agency that supports research and education in nonmedical fields, required that data management plans be submitted with all grant proposals. On February 22, 2013, the U.S. President's Office of Science and Technology Policy extended a similar requirement for all federal agencies with research and development budgets of more than $100 million. These requirements illustrate the need for further coordination and management of data as scholarship with traditional publications. Purdue University Libraries and its Joint Transportation Research Program (JTRP) collaborated to develop a comprehensive work flow that links technical report production with the management and publication of associated data. This paper illustrates early initiatives to integrate discrete data publications with traditional scholarly publications by leveraging new and existing repository platforms and services. The authors review government policies, past data-sharing practices, early pilot initiatives, and work flow integration between Purdue's data repository, the traditional press, and institutional repository. Through the adoption of these work flows, the authors propose best practices for integrating data publishing and dissemination into the research process. The implementation of this model has the potential to assist researchers in meeting the requirements of federal funding agencies, while reducing redundancy, ensuring integrity, expanding accessibility, and increasing the return on research investment.