Special Issue "Data Reuse for Sustainable Development Goals"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (20 March 2020).

Special Issue Editors

Prof. Dr. Fernanda Peset
E-Mail Website
Guest Editor
Information Science, Universitat Politècnica de València-IUMPA, 46022 València, Spain
Interests: open science; research data; open data; scholarly communication; data mining
Prof. Dr. Rafael Aleixandre-Benavent
E-Mail Website
Guest Editor
Spanish Research Council-CSIC
Interests: scientometrics; research data; science assessment; indicators
Prof. Dr. Antonia Ferrer-Sapena
E-Mail Website
Guest Editor
Information Science, Universitat Politècnica de València-IUMPA, 46022 València, Spain
Interests: scientometrics; research data; science assessment; indicators

Special Issue Information

Dear colleagues,

Research data and open data are of extreme value that is presently recognized at political and business levels. Extending its use may contribute to a socioeconomic change towards a data-based paradigm. The reuse of data takes place in a cross-disciplinary environment, in the same way as the challenges identified in the United Nations’ 2030 Agenda and the Sustainable Development Goals (SDG). SDGs have a global vocation, and like science, both seek equity, justice, and the inclusion of developing countries. Solving actual problems also goes through the reuse and combination of diverse resources. As data are so diverse and abundant (types of data, sources, main operators and users or technologies), a disciplinary approach is needed to understand their context. However, to face the global challenges of society requires data that are findable, accessible, interoperable, and reusable (FAIR) and as open as possible, scaling down the disciplinary and national silos.

Researchers and infrastructures are being adjusted to the new context. Experts and scientific authors have highlighted problems in the use of data, with some remaining weaknesses that should be reduced. At present, it is not within everyone´s reach to take advantage of the potential of research data, unless additional resources are invested. In this Special Issue, we aim to present papers that highlight experiences of use of this data-overloaded ecosystem and seek to provide scientists with useful approaches to deal with the SDG.

We are looking to put together a collection of papers that highlight the efforts for reusing data that have contributed to the Sustainable Development Goals (new models and methods, global and cross-disciplinary infraestructures, problems and successes). We welcome case studies that cross disciplinary borders (geodata, meteodata, statistical data) and specific tools from researchers for data analysis, visualization, management, etc.; or agencies (ODRA, DIGICOM, SDGMAP, etc.). We hope that through this Special Issue the community of data reusers will be made aware that, in order to solve the challenges of the planet, the barriers between disciplines must be broken down as well as focus on a more equitable and inclusive application of research results.

Prof. Dr. Fernanda Peset
Prof. Dr. Rafael Aleixandre-Benavent
Prof. Dr. Antonia Ferrer-Sapena
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data reuse
  • Sustanaible Development Goals
  • Inclusive science
  • Digital divide
  • Researchers engagement
  • Internet of FAIR data and services
  • Metadata and standards

Published Papers (6 papers)

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Article
Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems
Data 2020, 5(2), 35; https://doi.org/10.3390/data5020035 - 06 Apr 2020
Cited by 1 | Viewed by 1141
Abstract
In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried [...] Read more.
In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried out. So far, just the importance of data quality in RIS, the investigation of its dimensions and techniques for measuring, improving, and increasing data quality in RIS (such as data profiling, data cleansing, data wrangling, and text data mining) has been focused. With this work, we try to derive an answer to the question of the impact of data quality on the success of RIS user acceptance. An acceptance of RIS users is achieved when the research institutions decide to replace the RIS and replace it with a new one. The result is a statement about the extent to which data quality influences the success of users’ acceptance of RIS. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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Article
Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions
Data 2020, 5(2), 29; https://doi.org/10.3390/data5020029 - 27 Mar 2020
Cited by 4 | Viewed by 1151
Abstract
This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research. [...] Read more.
This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research. The purpose of the study was to identify the habits and current experiences of Spanish researchers in the health sciences in relation to the management and sharing of raw research data. Method: An electronic questionnaire composed of 40 questions divided into three blocks was designed. The three Section s contained questions on the following aspects: (A) personal information; (B) creation and reuse of data; and (C) preservation of data. The questionnaire was sent by email to a list of universities in Spain to be distributed among their researchers and professors. A total of 1063 researchers completed the questionnaire. More than half of the respondents (54.9%) lacked a data management plan; nearly a quarter had storage systems for the research group; 81.5% used personal computers to store data; “Contact with colleagues” was the most frequent means used to locate and access other researchers’ data; and nearly 60% of researchers stated their data were available to the research group and collaborating colleagues. The main fears about sharing were legal questions (47.9%), misuse or interpretation of data (42.7%), and loss of authorship (28.7%). The results allow us to understand the state of data sharing among Spanish researchers and can serve as a basis to identify the needs of researchers to share data, optimize existing infrastructure, and promote data sharing among those who do not practice it yet. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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Article
The Emergency Medicine Facing the Challenge of Open Science
Data 2020, 5(2), 28; https://doi.org/10.3390/data5020028 - 25 Mar 2020
Cited by 4 | Viewed by 1088
Abstract
(1) Background: The availability of research datasets can strengthen and facilitate research processes. This is specifically relevant in the emergency medicine field due to the importance of providing immediate care in critical situations as the very current Coronavirus (COVID-19) Pandemic is showing to [...] Read more.
(1) Background: The availability of research datasets can strengthen and facilitate research processes. This is specifically relevant in the emergency medicine field due to the importance of providing immediate care in critical situations as the very current Coronavirus (COVID-19) Pandemic is showing to the scientific community. This work aims to show which Emergency Medicine journals indexed in Journal Citation Reports (JCR) currently meet data sharing criteria. (2) Methods: This study analyzes the editorial policies regarding the data deposit of the journals in the emergency medicine category of the JCR and evaluates the Supplementary material of the articles published in these journals that have been deposited in the PubMed Central repository. (3) Results: It has been observed that 19 out of the 24 journals contained in the emergency medicine category of Journal Citation Reports are also located in PubMed Central (PMC), yielding a total of 5983 articles. Out of these, only 9.4% of the articles contain supplemental material. Although second quartile journals of JCR emergency medicine category have quantitatively more articles in PMC, the main journals involved in the deposit of supplemental material belong to the first quartile, of which the most used format in the articles is pdf, followed by text documents. (4) Conclusion: This study reveals that data sharing remains an incipient practice in the emergency medicine field, as there are still barriers between researchers to participate in data sharing. Therefore, it is necessary to promote dynamics to improve this practice both qualitatively (the quality and format of datasets) and quantitatively (the quantity of datasets in absolute terms) in research. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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Data Descriptor
The Fluctuation of Process Gasses Especially of Carbon Monoxide during Aerobic Biostabilization of an Organic Fraction of Municipal Solid Waste under Different Technological Regimes
Data 2020, 5(2), 40; https://doi.org/10.3390/data5020040 - 19 Apr 2020
Cited by 1 | Viewed by 907
Abstract
Carbon monoxide (CO) is an air pollutant commonly formed during natural and anthropogenic processes involving incomplete combustion. Much less is known about biological CO production during the decomposition of the organic fraction (OF), especially originating from municipal solid waste (MSW), e.g., during the [...] Read more.
Carbon monoxide (CO) is an air pollutant commonly formed during natural and anthropogenic processes involving incomplete combustion. Much less is known about biological CO production during the decomposition of the organic fraction (OF), especially originating from municipal solid waste (MSW), e.g., during the aerobic biostabilization (AB) process. In this dataset, we summarized the temperature and the content of process gases (including rarely reported carbon monoxide, CO) generated inside full-scale AB of an organic fraction of municipal solid waste (OFMSW) reactor. The objective of the study was to present the data of the fluctuation of CO content as well as that of O2, CO2, and CH4 in process gas within the waste pile, during the AB of the OFMSW. The OFMSW was aerobically biostabilized in six reactors, in which the technological regimes of AB were dependent on process duration (42–69 days), waste mass (391.02–702.38 Mg), the intensity of waste aeration (4.4–10.7 m3·Mg−1·h−1), reactor design (membrane-covered reactor or membrane-covered reactor with sidewalls) and thermal conditions in the reactor (20.2–77.0 °C). The variations in the degree of waste aeration (O2 content), temperature, and fluctuation of CO, CO2, and CH4 content during the weekly measurement intervals were summarized. Despite a high O2 content in all reactors and stable thermal conditions, the presence of CO in process gas was observed, which suggests that ensuring optimum conditions for the process is not sufficient for CO emissions to be mitigated. In the analyzed experiment, CO concentration was highly variable over the duration of the process, ranging from a few to over 1,500 ppm. The highest concentration of CO was observed between the second and fifth weeks of the test. The reactor B2 was the source of the highest CO production and average highest temperature. This study suggests that the highest CO productions occur at the highest temperature, which is why the authors believe that CO production has thermochemical foundations. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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Data Descriptor
METER.AC: Live Open Access Atmospheric Monitoring Data for Bulgaria with High Spatiotemporal Resolution
Data 2020, 5(2), 36; https://doi.org/10.3390/data5020036 - 08 Apr 2020
Cited by 2 | Viewed by 1752
Abstract
Detailed atmospheric monitoring data are notoriously difficult to obtain for some geographic regions, while they are of paramount importance in scientific research, forecasting, emergency response, policy making, etc. We describe a continuously updated dataset, METER.AC, consisting of raw measurements of atmospheric pressure, temperature, [...] Read more.
Detailed atmospheric monitoring data are notoriously difficult to obtain for some geographic regions, while they are of paramount importance in scientific research, forecasting, emergency response, policy making, etc. We describe a continuously updated dataset, METER.AC, consisting of raw measurements of atmospheric pressure, temperature, relative humidity, particulate matter, and background radiation in about 100 locations in Bulgaria, as well as some derived values such as sea-level atmospheric pressure, dew/frost point, and hourly trends. The measurements are performed by low-power maintenance-free nodes with common hardware and software, which are specifically designed and optimized for this purpose. The time resolution of the measurements is 5 min. The short-term aim is to deploy at least one node per 100 km2, while uniformly covering altitudes between 0 and 3000 m asl with a special emphasis on remote mountainous areas. A full history of all raw measurements (non-aggregated in time and space) is publicly available, starting from September 2018. We describe the basic technical characteristics of our in-house developed equipment, data organization, and communication protocols as well as present some use case examples. The METER.AC network relies on the paradigm of the Internet of Things (IoT), by collecting data from various gauges. A guiding principle in this work is the provision of findable, accessible, interoperable, and reusable (FAIR) data. The dataset is in the public domain, and it provides resources and tools enabling citizen science development in the context of sustainable development. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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Data Descriptor
Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions
Data 2020, 5(2), 32; https://doi.org/10.3390/data5020032 - 28 Mar 2020
Cited by 2 | Viewed by 1090
Abstract
Analysis of voltage–current behavior of photovoltaic modules is a critical part of their modeling. Parameter identification of these models demands data from them, measured in realistic environments. In spite of advancement in modeling methodologies under solar lighting, few analyses have been focused on [...] Read more.
Analysis of voltage–current behavior of photovoltaic modules is a critical part of their modeling. Parameter identification of these models demands data from them, measured in realistic environments. In spite of advancement in modeling methodologies under solar lighting, few analyses have been focused on indoor photovoltaics. Lack of accurate and reproducible data as a major challenge in this field is addressed here. A high accuracy measurement setup for evaluation and analysis of indoor photovoltaic modules is explained. By use of this system, different modules are measured under diverse environmental conditions. These measurements are structured in data-sets that can be used for either analysis of physical environment effects and modeling or development of specific parameter identification methods in low light intensity conditions. Full article
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
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