{"title":"Research information in the light of artificial intelligence: quality and data ecologies","authors":"Otmane Azeroual, Tibor Koltay","doi":"arxiv-2405.12997","DOIUrl":null,"url":null,"abstract":"This paper presents multi- and interdisciplinary approaches for finding the\nappropriate AI technologies for research information. Professional research\ninformation management (RIM) is becoming increasingly important as an expressly\ndata-driven tool for researchers. It is not only the basis of scientific\nknowledge processes, but also related to other data. A concept and a process\nmodel of the elementary phases from the start of the project to the ongoing\noperation of the AI methods in the RIM is presented, portraying the\nimplementation of an AI project, meant to enable universities and research\ninstitutions to support their researchers in dealing with incorrect and\nincomplete research information, while it is being stored in their RIMs. Our\naim is to show how research information harmonizes with the challenges of data\nliteracy and data quality issues, related to AI, also wanting to underline that\nany project can be successful if the research institutions and various\ndepartments of universities, involved work together and appropriate support is\noffered to improve research information and data management.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.12997","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents multi- and interdisciplinary approaches for finding the
appropriate AI technologies for research information. Professional research
information management (RIM) is becoming increasingly important as an expressly
data-driven tool for researchers. It is not only the basis of scientific
knowledge processes, but also related to other data. A concept and a process
model of the elementary phases from the start of the project to the ongoing
operation of the AI methods in the RIM is presented, portraying the
implementation of an AI project, meant to enable universities and research
institutions to support their researchers in dealing with incorrect and
incomplete research information, while it is being stored in their RIMs. Our
aim is to show how research information harmonizes with the challenges of data
literacy and data quality issues, related to AI, also wanting to underline that
any project can be successful if the research institutions and various
departments of universities, involved work together and appropriate support is
offered to improve research information and data management.
本文介绍了为研究信息寻找合适的人工智能技术的多学科和跨学科方法。专业研究信息管理(RIM)作为研究人员的明确数据驱动工具,正变得越来越重要。它不仅是科学知识流程的基础,还与其他数据相关。本文介绍了从项目开始到人工智能方法在 RIM 中持续运行的基本阶段的概念和流程模型,描绘了一个人工智能项目的实施过程,该项目旨在使大学和研究机构能够支持其研究人员处理不正确和不完整的研究信息,同时将这些信息存储到他们的 RIM 中。我们的目的是展示研究信息如何与与人工智能相关的数据扫盲和数据质量问题的挑战相协调,同时也希望强调,如果参与其中的研究机构和大学各部门通力合作,并提供适当的支持以改进研究信息和数据管理,那么任何项目都能取得成功。