![]() A good set of column titles will allow the reader to quickly grasp what the table is about. The reader’s attention moves from the title to the column title sequentially. Column Titles: The goal of these title headings is to simplify the table.The titles can be lengthy or short, depending on the discipline. Title: Tables should have a clear, descriptive title, which functions as the “topic sentence” of the table.Tables that are disorganized or otherwise confusing will make the reader lose interest in your work. As with academic writing, it is also just as important to structure tables so that readers can easily understand them. Tables have several elements, including the legend, column titles, and body. Effective data presentation in research papers requires understanding your reader and the elements that comprise a table. Tables and figures in scientific papers are wonderful ways of presenting data. Tables are easily created using programs such as Excel. How do you know if you need a table or figure? The rule of thumb is that if you cannot present your data in one or two sentences, then you need a table. When writing a research paper, the importance of tables and figures cannot be underestimated. An APA research paper and MLA research paper both require tables and figures, but the rules around them are different. There are many ways of presenting data in tables and figures, governed by a few simple rules. The data in figures and tables, however, should not be a repetition of the data found in the text. When writing a research paper, it is important for data to be presented to the reader in a visually appealing way. We therefore posit an agenda for data responsibility considering its collection and critical interpretation.Research papers are often based on copious amounts of data that can be summarized and easily read through tables and graphs. We contend that it is necessary to advance an open and honest discussion about the responsibilities of all stakeholders in the data ecosystem – collectors, researchers, and those interpreting and applying findings – to thoughtfully and transparently reflect on those biases use data in good faith and acknowledge limitations. As curators and analysts of large, popular data projects, we are uniquely aware of biases that are present when collecting and using event data. In conflict data, there are often perceptions of damaging bias, and skepticism can emanate from several areas, including confidence in whether data collection procedures create systematic omissions, inflations, or misrepresentations. All data have biases, which we define as an inclination, prejudice, or directionality to information. With increased availability of disaggregated conflict event data for analysis, there are new and old concerns about bias. We show the utility of MIHCSME for HCS data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing Findable, Accessible, Interoperable and Reusable (FAIR) bioimaging data throughout the Netherlands Bioimaging network. ![]() In addition, ISA compliance enables broader integration with other types of experimental data, paving the way for visual omics and multi-Omics integration. The tabular template provides an easy-to-use practical implementation of REMBI, specifically for High Content Screening (HCS) data. It has been developed by combining the ISA (Investigations, Studies, Assays) metadata standard with a semantically enriched instantiation of REMBI (Recommended Metadata for Biological Images). The Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) is a metadata model and reusable tabular template for sharing and integrating high content imaging data. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. ![]() Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. FAIR principles are now being adapted in the context of AI models and datasets. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
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