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ACM SIGMOD International Conference on Management of data (SIGMOD)

作者： Song, Jie Alter, George Jagadish, H. V. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450356435

datasets are often derived by manipulating raw data with statistical software packages. The derivation of a dataset must be recorded in terms of both the raw input and the manipulations applied to it. Statistics packages typically provide limited help in documenting provenance for the resulting derived data. At best, the operations performed by the statistical package are described in a script. Disparate representations make these scripts hard to understand for users. To address these challenges, we created Continuous Capture of Metadata (C(2)Metadata), a system to capture data transformations in scripts for statistical packages and represent it as metadata in a standard format that is easy to understand. We do so by devising a Structured data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a large fraction of data manipulation performed in practce. We then implement SDTA, inspired by relational algebra, in a data transformation specification language we call SDTL. In this demonstration, we showcase C(2)Metadata's capture of data transformations from a pool of sample transformation scripts in at least two languages: SPSS (R) and Stata (R)(SAS (R) and R are under development), for social science data in a large academic repository. We will allow the audience to explore C2Metadata using a web-based interface, visualize the intermediate steps and trace the provenance and changes of data at different levels for better understanding of the process.

关键词： data transformation data documentation data provenance

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The Venus score for the assessment of the quality and trustworthiness of biomedical datasets

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BIOdata MINING 2025年第1期18卷 1-31页

作者： Chicco, Davide Fabris, Alessandro Jurman, Giuseppe Univ Milano Bicocca Milan Italy Univ Toronto Toronto ON Canada Max Planck Inst Secur & Privacy Bochum Germany Fdn Bruno Kessler Trento Italy

Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets. Although generally useful, however, they are often incomplete and impractical. The guidelines of datasheets for datasets, in particular, are too numerous;the requirements of the Kaggle dataset Usability Score focus on non-scientific requisites (for example, including a cover image);and the European Union Artificial Intelligence Act (EU AI Act) sets forth sparse and general data governance requirements, which we tailored to datasets for biomedical AI. Against this backdrop, we introduce our new Venus score to assess the data quality and trustworthiness of biomedical datasets. Our score ranges from 0 to 10 and consists of ten questions that anyone developing a bioinformatics, medical informatics, or cheminformatics dataset should answer before the release. In this study, we first describe the EU AI Act, datasheets for datasets, and the Kaggle dataset Usability Score, presenting their requirements and their drawbacks. To do so, we reverse-engineer the weights of the influential Kaggle Score for the first time and report them in this study. We distill the most important data governance requirements into ten questions tailored to the biomedical domain, comprising the Venus score. We apply the Venus score to twelve datasets from multiple subdomains, including electronic health records, medical imaging, microarray and bulk RNA-seq gene expression, cheminformatics, physiologic electrogram signals, and medical text. Analyzing the result

关键词： Biomedical data quality data trustworthiness data documentation Medical data Health informatics Bioinformatics Trustworthiness Trustworthy data Computational biology Cheminformatics Medical text EU AI Act datasheets for datasets Kaggle

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Reduce, reuse, recycle: Introducing MetaPipeX, a framework for analyses of multi-lab data

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RESEARCH SYNTHESIS METHODS 2024年第6期15卷 1183-1199页

作者： Fuenderich, Jens H. Beinhauer, Lukas J. Renkewitz, Frank Univ Erfurt Dept Psychol Erfurt Germany Ludwig Maximilians Univ Munchen Dept Psychol Munich Germany

Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed and used in research synthesis. Their repositories and code could provide guidance to future projects of this kind. But, while multi-labs are similar in their structure and aggregate their data using meta-analyses, they deploy a variety of different solutions regarding the storage structure in the repositories, the way the (analysis) code is structured and the file-formats they provide. Continuing this trend implies that anyone who wants to work with data from multiple of these projects, or combine their datasets, is faced with an ever-increasing complexity. Some of that complexity could be avoided. Here, we introduce MetaPipeX, a standardized framework to harmonize, document and analyze multi-lab data. It features a pipeline conceptualization of the analysis and documentation process, an R-package that implements both and a Shiny App (https://***/metapipex/) that allows users to explore and visualize these data sets. We introduce the framework by describing its components and applying it to a practical example. Engaging with this form of collaboration and integrating it further into research practice will certainly be beneficial to quantitative sciences and we hope the framework provides a structure and tools to reduce effort for anyone who creates, re-uses, harmonizes or learns about multi-lab replication projects.

关键词： code data documentation data visualization experiment meta-analysis multi-lab

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Structured data transformation algebra (SDTA) and its applications

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DISTRIBUTED AND PARALLEL dataBASES 2022年第2-3期40卷 373-408页

作者： Song, Jie Alter, George Jagadish, H., V Univ Michigan Comp Sci Engn Ann Arbor MI 48109 USA Univ Michigan ICPSR Ann Arbor MI 48109 USA

Statistical analysis is a crucial component of many data science analytic pipelines, and preparing data for such analysis is a large part of the data ingestion step. This task is generally accomplished by writing transformation scripts in languages such as SPSS, Stata, SAS, R, Python (Pandas) etc. The disparate data models, language representations and transformation operations supported by these tools make it hard for end users to understand and document the transformations performed, and for developers to port transformation code across languages. Tackling these challenges, we present a formal paradigm for statistical data transformation called SDTA and embody in a language called SDTL. Experiments with real statistical transformations on socio-economic data show that SDTL can successfully represent 86.1% and 91.6% respectively of 4185 commands in SAS and 9087 commands in SPSS obtained from a repository. We illustrate how SDTA/SDTL could assist with the documentation of statistical data transformation, an important aspect often neglected in metadata of datasets. We propose a system called C(2)Metadata that automatically captures the transformation and provenance information in SDTL as a part of the metadata. Moreover, given the conversion mechanism from a source statistical language to SDTA/SDTL, we show how a data transformation program could be converted to other functionally equivalent programs, permitting code reuse and result reproducibility. We also illustrate the possibility of using SDTA to optimize SDTL transformations using rule-based rewrites similar to SQL optimizations.

关键词： data transformation algebra Statistical data data documentation

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Completeness of datasets documentation on ML/AI Repositories: An Empirical Investigation 22nd

Completeness of Datasets Documentation on ML/AI Repositories...

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22nd EPIA Conference on Artificial Intelligence (EPIA)

作者： Rondina, Marco Vetro, Antonio De Martin, Juan Carlos Politecn Torino Turin Italy

ISBN: (纸本)9783031490071;9783031490088

ML/AI is the field of computer science and computer engineering that arguably received the most attention and funding over the last decade. data is the key element of ML/AI, so it is becoming increasingly important to ensure that users are fully aware of the quality of the datasets that they use, and of the process generating them, so that possible negative impacts on downstream effects can be tracked, analysed, and, where possible, mitigated. One of the tools that can be useful in this perspective is dataset documentation. The aim of this work is to investigate the state of dataset documentation practices, measuring the completeness of the documentation of several popular datasets in ML/AI repositories. We created a dataset documentation schema-the documentation Test Sheet (dts)-that identifies the information that should always be attached to a dataset (to ensure proper dataset choice and informed use), according to relevant studies in the literature. We verified 100 popular datasets from four different repositories with the dts to investigate which information were present. Overall, we observed a lack of relevant documentation, especially about the context of data collection and data processing, highlighting a paucity of transparency.

关键词： data documentation AI transparency AI accountability

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Third Workshop on Building towards Information Interaction and Retrieval Resources Re-use (BIIRRR 2022) 7

Third Workshop on Building towards Information Interaction a...

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7th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR)

作者： Bogers, Toine Gade, Maria Hall, Mark Michael Koolen, Marijn Petras, Vivien Thomas, Paul Aalborg Univ Copenhagen Denmark Humboldt Univ Berlin Germany Open Univ Milton Keynes Bucks England Huygens ING KNAW Amsterdam Netherlands Microsoft Canberra ACT Australia

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A Manifesto on Resource Re-Use in Interactive Information Retrieval 6

A Manifesto on Resource Re-Use in Interactive Information Re...

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6th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR)

作者： Gaede, Maria Koolen, Marijn Hall, Mark Bogers, Toine Petras, Vivien Humboldt Univ Berlin Germany Royal Netherlands Acad Arts & Sci Amsterdam Netherlands Open Univ Milton Keynes Bucks England Aalborg Univ Copenhagen Denmark

ISBN: (纸本)9781450380553

This perspective paper on resource re-use intends to draw the attention of the interactive information retrieval (IIR) community to the challenges of research documentation and archiving for future use. Resources are understood as encompassing research designs, research data and research infrastructures. It proposes eight principles for improving the re-use of resources in the IIR community and presents concrete steps on how to achieve them. A five-level system for data archiving and documentation envisions increasingly open and stable documentation and access infrastructures.

关键词： Research data research design research software re-use data documentation data sharing practices

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SDTA: An Algebra for Statistical data Transformation 2021

SDTA: An Algebra for Statistical Data Transformation

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33rd International Conference on Scientific and Statistical database Management (SSDBM)

作者： Song, Jie Jagadish, H., V Alter, George Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450384131

Statistical data manipulation is a crucial component of many data science analytic pipelines, particularly as part of data ingestion. This task is generally accomplished by writing transformation scripts in languages such as SPSS, Stata, SAS, R, Python (Pandas) and etc. The disparate data models, language representations and transformation operations supported by these tools make it hard for end users to understand and document the transformations performed, and for developers to port transformation code across languages. Tackling these challenges, we present a formal paradigm for statistical data transformation. It consists of a data model, called Structured data Transformation data Model (SDTDM), inspired by the data models of multiple statistical transformations frameworks;an algebra, Structural data Transformation Algebra (SDTA), with the ability to transform not only data within SDTDM but also metadata at multiple structural levels;and an equivalent descriptive counterpart, called Structured data Transformation Language (SDTL), recently adopted by the DDI Alliance that maintains international standards for metadata as part of its suite of products. Experiments with real statistical transformations on socio-economic data show that SDTL can successfully represent 86.1% and 91.6% respectively of 4,185 commands in SAS and 9,087 commands in SPSS obtained from a repository. We illustrate with examples how SDTA/SDTL could assist with the documentation of statistical data transformation, an important aspect often neglected in metadata of datasets. We propose a system called C(2)Metadata that automatically captures the transformation and provenance information in SDTL as a part of the metadata. Moreover, given the conversion mechanism from a source statistical language to SDTA/SDTL, we show how functional-equivalent transformation programs could be converted to other functionally equivalent programs, in the same or different language, permitting code reuse and result re

关键词： data transformation algebra statistical data data documentation

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Archaeological Analysis in the Information Age: Guidelines for Maximizing the Reach, Comprehensiveness, and Longevity of data

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ADVANCES IN ARCHAEOLOGICAL PRACTICE 2020年第1期8卷 40-52页

作者： Kansa, Sarah W. Atici, Levent Kansa, Eric C. Meadow, Richard H. Open Context 125 El Verano Way San Francisco CA 94127 USA Univ Nevada Dept Anthropol 4505 S Maryland Pkwy Las Vegas NV 89154 USA Harvard Univ Dept Anthropol 11 Divin Ave Cambridge MA 02138 USA

With the advent of the Web, increased emphasis on "research data management," and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities.

关键词： zooarchaeology data management reproducible research data documentation guidelines data reuse

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How to Automatically Document data With the codebook Package to Facilitate data Reuse

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ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE 2019年第2期2卷 169-187页

作者： Arslan, Ruben C. Max Planck Inst Human Dev Ctr Adapt Rat D-14195 Berlin Germany

data documentation in psychology lags behind not only many other disciplines, but also basic standards of usefulness. Psychological scientists often prefer to invest the time and effort that would be necessary to document existing data well in other duties, such as writing and collecting more data. Codebooks therefore tend to be unstandardized and stored in proprietary formats, and they are rarely properly indexed in search engines. This means that rich data sets are sometimes used only once-by their creators-and left to disappear into oblivion. Even if they can find an existing data set, researchers are unlikely to publish analyses based on it if they cannot be confident that they understand it well enough. My codebook package makes it easier to generate rich metadata in human- and machine-readable codebooks. It uses metadata from existing sources and automates some tedious tasks, such as documenting psychological scales and reliabilities, summarizing descriptive statistics, and identifying patterns of missingness. The codebook R package and Web app make it possible to generate a rich codebook in a few minutes and just three clicks. Over time, its use could lead to psychological data becoming findable, accessible, interoperable, and reusable, thereby reducing research waste and benefiting both its users and the scientific community as a whole.

关键词： individual differences coding data visualization reliability codebook metadata data dictionary data documentation open materials

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