Data Creators Research Articles

Querying feminicide data in Mexico

The full extent of feminicide in Mexico remains unknown. When available, data on the gender-related killing of women and girls are often incomplete, inaccurate, or inexplicable. In this article, a sociologist (Saide) and a statistician (Maria) query feminicide data in Mexico. Drawing on Timnit Gebru et al.’s ‘datasheets for datasets’ and Sarah Holland et al.’s ‘data nutrition label’ frameworks, we zoom in on the two primary governmental sources measuring feminicide in the country, the mortality records processed by the Instituto Nacional de Estadística, Geografía e Informática (INEGI) and the alleged feminicide investigation files published by the Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública (SESNSP). In the discussion, we shed light on two noteworthy remarks. First, the discordance between INEGI and SESNSP data, whereby we outline four crucial variations: naming, underreporting, comparability, and availability. Second, the shortcomings of these data sources in measuring feminicide as we understand it sociologically. In other words, neither explicitly gauge the ‘gender-related’ motivation underlying the crime. Instead, what data from INEGI and SESNSP currently provide us with are discordant approximations of the phenomenon, aligning with what Sandra Walklate and Kate Fitz-Gibbon define as ‘thin’ feminicide counts. This contribution seeks to act as a guide to better understand feminicide data in Mexico, to enhance effective communication between data creators and users concerned with data-making practices, and to ignite the querying of data engaging with social justice and accountability against feminicide and beyond.

Actually Accessible Data: An Update and a Call to Action

As funder, journal, and disciplinary norms and mandates have foregrounded obligations of data sharing and opportunities for data reuse, the need to plan for and curate data sets that can reach researchers and end-users with disabilities has become even more urgent. We begin by exploring the disability studies literature, describing the need for advocacy and representation of disabled scholars as data creators, subjects, and users. We then survey the landscape of data repositories, curation guidelines, and research-data-related standards, finding little consideration of accessibility for people with disabilities. We suggest three sets of minimal good practices for moving toward truly accessible research data: 1) ensuring Web accessibility for data repositories; 2) ensuring accessibility of common text formats, including those used in documentation; and 3) enhancement of visual and audiovisual materials. We point to some signs of progress in regard to truly accessible data by highlighting exemplary practices by repositories, standards, and data professionals. Accessibility needs to become a mainstream component of curation practice included in every training, manual, and primer.

Cartography & geovisual analytics in personal contexts: designing for the data creator

ABSTRACT With the rise of the Quantified-Self movement and widespread adoption of self tracking technologies, 'personal' has become a new dimension in the map and geovisualization design process. Despite a rich cartographic literature on how to map movement data, and an equally extensive geovisual analytics literature on how to make sense of complex visual representations of movement data at scale, opportunities exist to adapt and create new map and interaction design paradigms to meet the disparate needs of an ever-growing number of data creators. This paper presents a cross-disciplinary review of literature that reports on the design and evaluation of personal geovisualizations. Specifically, this review paper: contextualizes individual movement data types and provides a short synthesis of cartographic and geovisual analytics approaches that have been employed to explore such data in non-personal contexts; surveys a growing yet disconnected body of foundational work beyond the GIScience discipline; presents a detailed discussion on a collection of works that exemplifies the potential of applying a geographic perspective to personal visualization; and outlines key challenges and opportunities for advancing the state-of-the-art & science in the design of maps and geovisual analytics applications constructed from personal data and that benefit the data creator.

Promoting data quality and reuse in archaeology through collaborative identifier practices

Investments in data management infrastructure often seek to catalyze new research outcomes based on the reuse of research data. To achieve the goals of these investments, we need to better understand how data creation and data quality concerns shape the potential reuse of data. The primary audience for this paper centers on scientific domain specialists that create and (re)use datasets documenting archaeological materials. This paper discusses practices that promote data quality in support of more open-ended reuse of data beyond the immediate needs of the creators. We argue that identifier practices play a key, but poorly recognized, role in promoting data quality and reusability. We use specific archaeological examples to demonstrate how the use of globally unique and persistent identifiers can communicate aspects of context, avoid errors and misinterpretations, and facilitate integration and reuse. We then discuss the responsibility of data creators and data reusers to employ identifiers to better maintain the contextual integrity of data, including professional, social, and ethical dimensions.

The Vision of the FAIR Digital Object Machine and Ubiquitous FDO Services

The Vision of the FAIR Digital Object Machine and Ubiquitous FDO Services

Fostering habits of care: Reframing qualitative data sharing policies and practices

This article argues that navigating the formalized, regulated and institutionalized data sharing landscape is challenging for researchers tasked with making qualitative data available for re-use. Archiving empirical material has progressively become a requirement enshrined in the policies of UK research institutions. Yet, how qualitative researchers feature – as data (co)creators and curators – within a process governed largely by quantitative data management strategies remains undocumented. Using examples from the ESRC Timescapes initiative, this article argues that to advance ethical practice in qualitative secondary analysis (QSA), data sharing policies and practices need to be re-framed to respect, value and care for the particularities of qualitative data and the emotional, intellectual and temporal investments made by qualitative researchers working in an increasingly pressurized Higher Education (HE) environment. Accordingly, ideas from the ethics of care literature are employed to propose areas where ‘habits of care’, attuned to the needs of qualitative researchers and data, can be fostered.

Information needs on research data creation

Researchers’ data related information needs are growing. This paper reports the findings of a study with archaeologists and cultural heritage professionals focussing on data reuse related meta-information needs.Methods. Interviews with (N=)10 archaeologists and cultural heritage professionals. Qualitative coding and content analysis. Four types of paradata needs (data on processes, e.g. data creation) are identified, including 1) scope, 2) provenance, 3) methods and 4) knowledge organisation and representation paradata. Knowledge organisation and representation paradata has been least explored both in research and practises so far. The findings point to a need to develop the understanding of the needs and means of documentation of knowledge organisation and representation. The findings contribute to the data literacy of researchers producing and using data descriptions, and to the study of how paradata can be created and used. Further, the findings indicate that distance-to-data is a significant parameter in determining whether information needs are continuous or discrete. Further, the most likely type of reuse should guide the level and type of paradata. Finally, the findings underline that in spite of the comprehensiveness of available meta-information, it will be incomplete. Complementary means — including collaboration with data creators and meta-information extraction approaches — are needed to increase information reusability.

Data Shepherding in Nanotechnology: An Antimicrobial Functionality Data Capture Template

In this paper, we exhibit how to construct a template for capturing antimicrobial capacity data of nanomaterials or nanoenabled products. The template promotes the principles of making data scientifically findable, accessible, interoperable and reusable (FAIR), encouraging scientists to reuse it. The template construction roadmap entails the following steps: (1) recognize appropriate stakeholders, (2) allocate surveys to collect a general explanation of the data that will be created, (3) comprehend each stakeholder’s requirements, (4) cooperating and using straightforward communication with the participants for the selection of the minimum data requirement reporting and (5) template layout and ontological annotation. We provide an annotated template for capturing antimicrobial data, increasing their interoperability while populating it with real measurements as an example. By applying the roadmap or by utilizing the template portrayed herein, in the case of a safe-by-design nanoproject (Anticipating Safety Issues at the Design of Nano Product Development (ASINA)), data creators of antimicrobial assessments can store the data using the FAIR approach. Furthermore, data shepherds and scientists can skip the lengthy template generation process and speed up the community’s progress on the FAIR route.

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template.

In this paper, we demonstrate the realization process of a pragmatic approach on developing a template for capturing field monitoring data in nanomanufacturing processes. The template serves the fundamental principles which make data scientifically Findable, Accessible, Interoperable and Reusable (FAIR principles), as well as encouraging individuals to reuse it. In our case, the data shepherds’ (the guider of data) template creation workflow consists of the following steps: (1) Identify relevant stakeholders, (2) Distribute questionnaires to capture a general description of the data to be generated, (3) Understand the needs and requirements of each stakeholder, (4) Interactive simple communication with the stakeholders for variables/descriptors selection, and (5) Design of the template and annotation of descriptors. We provide an annotated template for capturing exposure field campaign monitoring data, and increase their interoperability, while comparing it with existing templates. This paper enables the data creators of exposure field campaign data to store data in a FAIR way and helps the scientific community, such as data shepherds, by avoiding extensive steps for template creation and by utilizing the pragmatic structure and/or the template proposed herein, in the case of a nanotechnology project (Anticipating Safety Issues at the Design of Nano Product Development, ASINA).

Metadata Made Easy: Develop and Use Domain-Specific Metadata Schemes by following the dmdScheme approach.

Metadata plays an essential role in the long‐term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain‐specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co‐development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet.We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain‐specific scheme. We describe how to create a domain‐specific scheme from this template, including the iterative co‐development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation.The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML.Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain‐specific schemes. A near‐future priority is to provide methods to interface domain‐specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.

Data Shepherding in Nanotechnology. The Initiation.

In this paper we describe the pragmatic approach of initiating, designing and implementing the Data Management Plan (DMP) and the data FAIRification process in the multidisciplinary Horizon 2020 nanotechnology project, Anticipating Safety Issues at the Design Stage of NAno Product Development (ASINA). We briefly describe the general DMP requirements, emphasizing that the initial steps in the direction towards data FAIRification must be conceptualized and visualized in a systematic way. We demonstrate the use of a generic questionnaire to capture primary data and metadata description from our consortium (data creators/experimentalists and data analysts/modelers). We then display the interactive process with external FAIR data initiatives (data curators/quality assessors), regarding guidance for data and metadata capturing and future integration into repositories. After the preliminary data capturing and FAIRification template is formed, the inner-communication process begins between the partners, which leads to developing case-specific templates. This paper assists future data creators, data analysts, stewards and shepherds engaged in the multi-faceted data shepherding process, in any project, by providing a roadmap, demonstrated in the case of ASINA.

Red Sky at Night: digital archiving in England 2020

Across Europe, countries are at different stages of dealing with digital archaeological archives and how those archives are dealt with is affected by differences in statutory requirements, legal ownership of archives and infrastructure. In England, there is no single national repository for archaeological archives and there is no legal requirement to deposit either physical or digital archives with public bodies, and deposition rates for digital remain low. The archaeological sector remains aware of the implications of producing ever-growing quantities of born-digital data, which interested parties are attempting to address. The current framework and situation regarding digital archiving in England, therefore, offers an interesting case study for how digital archiving is dealt with through the application of professional standards, rather than legislative requirements. This article aims to summarise the current situation regarding digital archaeological archives in England. Across the UK there has been a great deal of work and focus on archaeological archives, driven by two main factors: archaeological archives are curated by a network of regional and local museums and currently resources are shrinking in terms of storage capacity and archaeological curators. Repositories for physical archives can therefore be ill-prepared for the curation of digital archaeological archives, which can risk loss of archaeological digital data, and in 2017 this was recognised within the UK government's Mendoza Review. This article describes the current work to resolve these issues - including strengthening the planning process, providing standards and guidance for data creators and repositories.

Accessibility of environmental data for sharing: The role of UX in large cyberinfrastructure projects

Incorporating user experience (UX) testing when creating research cyberinfrastructure is often overlooked, but if left too late, the cost of retrofitting is considerable, and the very clients the cyberinfrastructure was built to serve may be lost. Successfully integrating UX testing into the product development cycle can be difficult but rewarding. This paper describes how UX evaluations were incorporated over ten years of operation of DataONE (www.dataone.org), a multi-sector science research cyberinfrastructure project created to support the discovery, access, and sustainability of data about life on Earth and the environment that sustains it. The diverse stakeholders in DataONE include data creators and users such as researchers and government workers across the broad scope of the earth and environmental sciences as well as those who hold and manage data such as libraries and data repositories. Between 2009 and 2019 DataONE members designed and constructed data management tools and services to fulfill the DataONE objectives. To assist in achieving its goals, a participatory design approach was used by establishing several largely volunteer and stakeholder-representative working groups, including the Usability and Assessment Working Group. This Working Group conducted over forty UX evaluations to assess the usability of DataONE products and websites at various stages of the development process. In addition to improving the usability of DataONE products, the UX evaluations fostered community involvement by building trust and engagement with the products being developed. The DataONE UX experience yields several important lessons which will improve the success of other projects. It is our conclusion that UX testing should be a mandatory part of the design of any cyberinfrastructure project.

Data Curator in the Middle: Curating Data for a Diverse Community of Stakeholders


 The Prevention and Early Intervention Research Initiative is an archiving project to preserve the data and reports that were generated by twelve years of philanthropic and state investment into prevention and early intervention approaches in the children and youth sector in Ireland and Northern Ireland. The investment resulted in an extensive collection of evaluation data and reports, which collectively provide an evidence base for continued investment into PEI programmes that are shown to be effective. In 2016, the Prevention and Early Intervention Research Initiative (PEI-RI) was established to preserve the outputs from these evaluations in the national data archives, as a publicly available evidence base. The political and social significance of this collection is manifest in the range of stakeholder groups that the project is engaging with, including the community and not-for-profit organisations that operated the PEI programmes, the research teams from academic institutions that evaluated these programmes, and representatives from government departments that co-funded many of these programmes with Atlantic.
 This paper tells the story of the PEI-RI archiving project, describing the steps we’ve taken since 2016 to preserve and promote the PEI data. During the course of the project we realised that it would not be enough to provide access to the data alone, as "[g]enerating and collating the evidence is of no use if it never reaches the commissioners and professionals who need it" (What Works Network, 2014, pp. 6). In the second phase of our project we are creating a range of resources for practitioner and decision maker audiences which provide a pathway to the data using the archival infrastructure.
 The project provides a case study of curating a digital collection that is intended for multiple stakeholders with different expectations of the archived material. The PEI-RI data curator is located in the middle of a triad of data creators, data consumers and data archives, and is tasked with balancing the interests, expectations and limitations of each.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data.

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

The Application of E-ARK Tools for Archival Interoperability to Support a Long-term Sustainable Digital Single Market

ABSTRACT European Union support for the development of digital cross-border means for conducting business and running governments has fundamentally changed how public and private business is conducted. But this digital information has to remain available for long periods, often beyond the life expectancy of individual information systems or technological components. This poses a significant challenge for both the original data creators and digital archives that have to be capable of both dealing with the most current information technology, and with “saving” information from legacy systems. To deal with these challenges, it is necessary to bridge the gap between the methods used in digital preservation and content creation and introduce additional practical interoperability into the solutions used for a Digital Single Market and digital preservation infrastructures. The European Commission recognized the need in 2013 and funded the E-ARK project (2014–2017), which successfully delivered a set of principles, specifications and tools that provided an interoperable and international solution for the sustainable long-term availability of digital information. This paper describes the solutions developed by the E-ARK project, focusing on the SIP format, as well as the steps the European Commission is taking toward building a thriving digital preservation community around the E-ARK outcomes.

Data journals: incentivizing data access and documentation within the scholarly communication system

Data journals provide strong incentives for data creators to verify, document and disseminate their data. They also bring data access and documentation into the mainstream of scholarly communication, rewarding data creators through existing mechanisms of peer-reviewed publication and citation tracking. These same advantages are not generally associated with data repositories, or with conventional journals’ data-sharing mandates. This article describes the unique advantages of data journals. It also examines the data journal landscape, presenting the characteristics of 13 data journals in the fields of biology, environmental science, chemistry, medicine and health sciences. These journals vary considerably in size, scope, publisher characteristics, length of data reports, data hosting policies, time from submission to first decision, article processing charges, bibliographic index coverage and citation impact. They are similar, however, in their peer review criteria, their open access license terms and the characteristics of their editorial boards.

Using historical data to explore transactional data quality of an African power generation company

Background: In developing countries, despite large public companies’ reliance on master data for decision-making, there is scant evidence to demonstrate their effective use of transactional data in decision-making because of its volatility and complexity. For the state-owned enterprise (SOE) studied, the complexity of generating high-quality transactional data manifests in relationships between customer call transactional data related to an electricity supply problem (captured by call centre agents, i.e. data creators) and technician-generated feedback (i.e. data consumers). Objectives: To establish the quality of customer calls transactional data captured using source system measurements. To compare this data set with field technicians’ downstream system transactions that indicated incorrect transactional data. Method: The study compared historical customer calls transactional data (i.e. source system data) with field technician-generated feedback captured on work orders (i.e. receiving system) in a power generation SOE, to ascertain transactional data quality generated and whether field technicians responded to authentic customer calls exclusively to mitigate operational expenses. Results: Mean values of customer call transactional data quality from the source system and technician-generated feedback on work orders varied by 1.26%, indicating that data quality measurements at the source system closely resembled data quality experiences of data consumers. The SOE’s transactional data quality from the source system was 80.05% and that of historical data set from evaluating feedback was 81.31% – percentages that exceeded average data quality measurements in literature. Conclusion: Using a feedback control system (FCS) to integrate feedback generated by data consumers to data creators presents an opportunity to increase data quality to higher levels than its current norm.

Sentiment Analysis in Health and Well-Being: Systematic Review.

BackgroundSentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.”ObjectiveThis study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals.MethodsOur methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation.ResultsThe majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes.ConclusionsSA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

Uses and Reuses of Scientific Data: The Data Creators’ Advantage

Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers. We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves. By conducting a qualitative meta-analysis of evidence on two long-term, distributed, interdisciplinary consortia, we found that scientists frequently sought data from public collections and from other researchers for comparative purposes such as “ground-truthing” and calibration. When they sought others’ data for reanalysis or for combining with their own data, which was relatively rare, most preferred to collaborate with the data creators. We propose a typology of data reuses ranging from comparative to integrative. Comparative data reuse requires interactional expertise, which involves knowing enough about the data to assess their quality and value for a specific comparison such as calibrating an instrument in a lab experiment. Integrative reuse requires contributory expertise, which involves the ability to perform the action, such as reusing data in a new experiment. Data integration requires more specialized scientific knowledge and deeper levels of epistemic trust in the knowledge products. Metadata, ontologies, and other forms of curation benefit interpretation for any kind of data reuse. Based on these findings, we theorize the data creators’ advantage, that those who create data have intimate and tacit knowledge that can be used as barter to form collaborations for mutual advantage. Data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources, and institutions. Keywords: data, science, reuse, biomedicine, environmental sciences, open science, data practices, science policy