Introducing the Heritage Science Data Service

The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics.

<p>Critical Zone science studies the system of coupled chemical, biological, physical, and geological processes operating together across all scales to support life at the Earth's surface (Brantley et al., 2007). In 2020, the  U.S. National Science Foundation funded 10 Critical Zone Collaborative Network awards. These 5-year projects will collaboratively work to answer scientific questions relevant to understanding processes in the Critical Zone such as the effects of urbanization on Critical Zone processes; Critical Zone function in semi-arid landscapes and the role of dust in sustaining these ecosystems; processes in deep bedrock and their relationship to Critical Zone evolution; the recovery of the Critical Zone from disturbances such as fire and flooding; and changes in the coastal Critical Zone related to rising sea level. In order to support community data collection, access, and archival  for the Critical Zone Network community, the development of new cyberinfrastructure (CI) is now underway that leverages prior investments in domain-specific data repositories that are already operational and delivers data services to established communities. The goal is to create the infrastructure required for managing, curating, disseminating, and preserving data from the new network of Critical Zone Cluster projects, along with legacy datasets from the existing Critical Zone Observatory Network, including digital management of physical samples. This CI will have a distributed architecture that links existing data facilities and services, including HydroShare, EarthChem, SESAR (System for Earth Sample Registration), and eventually other systems like OpenTopography as needed, via a central CZ Hub that provides tools and services for simplified data submission, integrated data discovery and access, and links to computational resources for data analysis and visualization in support of CZ synthesis efforts. Our goal is to make data, samples, and software collected by the CZ Network Cluster projects Findable, Accessible, Interoperable, and Reusable following the FAIR guiding principles for scientific data management and stewardship, by taking advantage of existing, FAIR compliant, domain-specific data repositories. This collaboration among domain repositories to deliver integrated data services for an interdisciplinary science program will provide a template for future development of integrated interdisciplinary data services.</p>

Spectroscopic methods based on terahertz (THz) radiation can represent an innovative approach in the Cultural Heritage field. Specifically, contemporary and historical pigments can exhibit unique fingerprints. However, despite the high selectiveness of terahertz technique, the origin of spectral characteristics is not often known. In this work, the characteristic terahertz-time-domain spectroscopy spectrum of atacamite is reported in the spectral range 0.1–3.7 THz along with ab initio calculation of lattice dynamics to assess the nature of experimental features. The obtained results enable the acquisition of reliable new data in Heritage sciences, which is essential for developing accurate reference pigment databases.

Lessons learned from planetary science archiving

This paper describes a system for documenting scientific data produced in Heritage Sciences. The system is built around a general meta-model, flexible enough to provide descriptions, in a formal language, of the datasets produced by scientific research. Resulting metadata can be re-encoded and published in multiple formats. The underlying metadata schema is inspired by CIDOC CRM principles for data modelling and maintains a full compatibility with CIDOC CRM ontology to capture provenance and foster interoperability with Cultural Heritage information. The use of a wide set of thesauri and controlled vocabularies guarantees internal coherence at data and metadata level. Applicability tests are underway at different institutions and a set of user interfaces has been designed to simplify and speed up the process of data gathering and metadata definition.

High volumes of wide varieties of valuable data of different veracities can be easily generated or collected at a high velocity from various big data applications and services. Embedded in these big data are valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms return large numbers of frequent patterns, of which only a small portion may be of interest to users. In this paper, we present a constrained mining algorithm that allows crowds of users to collaboratively vote for their interesting patterns. Such an algorithm takes the benefits of crowdsourcing, crowdvoting and collaborative filtering for the data analytics and mining of popular constrained frequent patterns from big data applications and services.

The promotion of cities has usually been done by means of “attractiveness’ indicators.” “The New Economic Geography” and the book of Richard Florida about “The Rise of the Creative Class” have attempted to justify the choice of indicators of new forms of attractiveness. New dashboards containing indicators of the context (infrastructures and other factors) and, since Florida works (2002, 2005), local creative and innovative power indexes are added to the ancient territorial profiles. The competition between town centers and large and medium size cities is now illustrated with methods of ranking surveyed by OECD (OECD 2006). The emergence of digital societies is now analyzed as a structural change (Merryl, 2016). Some cities could now be beneficiary of the presence of big data actors or big data software and services (Townsend, 2013). From Los Angeles to a lot of so-called “smarter cities”( http://www.smart-cities.eu/) in Europe and in the world, the sharing and processing of public and private data is supposed to be more and more a key-factor of potential development, so of attractiveness (Batty, 2013, USC, 2014). So we will ask whether the traditional attractiveness’ ranking for cities should take account of the context of smarter cities embedded in a digital society. A lot of publications wants to deal with answers to this question (Stock Journal of the American Society for Information Science and Technology, 62(5), 963–986, 2011, Anthopoulos and Tougountzolou, 2011, Yigitcanlar ITU Journal of the Faculty of Architecture, 8(1), 53–67, 2011, Cohen, 2013). Taking account of the emergence of the digital society and of the digital divide between cities, this article propose to complement the approach of new cities’ attractiveness by adding two following supplementary dimensions of interest in the ranking and outranking of cities. These new dimensions are the location in some cities of a human potential constituted by data scientists (new criterion 1) and the existence of software capacities huge sharing and processing of big data by means of interoperable data platforms (new criterion 2). The article analyzes the relevance of these new criteria and concludes by a call for new empirical testing of the correlation between these criteria and the local productivity and welfare.

Currently, data are stored in an always-on condition, and can be globally accessed at any point, by any user. Data librarianship has its origins in the social sciences. In particular, the creation of data services and data archives, in the United Kingdom (Data Archives Services) and in the United States and Canada (Data Library Services), is a key factor for the emergence of data librarianship. The focus of data librarianship nowadays is on the creation of new library services. Data librarians are concerned with the proposition of services for data management and curation in academic libraries and other research organizations. The purpose of this paper is to understand how the complexity of the data can serve as the basis for identifying the technical skills required by data librarians. This essay is systematically divided, first introducing the concepts of data and research data in data librarianship, followed by an overview of data science as a theory, method, and technology to assess data. Next, the identification of the competencies and skills required by data scientists and data librarians are discussed. Our final remarks highlight that data librarians should understand that the complexity and novelty associated with data science praxis. Data science provides new methods and practices for data librarianship. A data librarian need not become a programmer, statistician, or database manager, but should be interested in learning about the languages and programming logic of computers, databases, and information retrieval tools. We believe that numerous kinds of scientific data research provide opportunities for a data librarian to engage with data science.

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

This issue’s editorial focuses on the response of librarians and information professionals to the United States government’s open access mandates. Librarians and information professionals realize that developing strong data science education curriculum and data literacy skills, dialoging with each other about shared staffing goals, and discovering which data services an institution needs, are vital in helping institutions be able to meet these mandates.

Purpose Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.Design/methodology/approach A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.Findings LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.Research limitations/implications The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.Originality/value This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.

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Abstract. This paper was initiated by a multidisciplinary Topic Workshop in the frame of the Deutsche Forschungsgemeinschaft Priority Program 1158 “Antarctic Research with Comparative Investigations in Arctic Ice Areas”, and hence it represents only the national view without claiming to be complete but is intended to provide awareness and suggestions for the current discussion on so-called big data in many scientific fields. The importance of the polar regions and their essential role for the Earth system are both undoubtedly recognized. However, dramatic changes in the climate and environment have been observed first in the Arctic and later in Antarctica over the past few decades. While important data have been collected and observation networks have been built in Antarctica and the Southern Ocean, this is a relatively data-scarce region due to the challenges of remote data acquisition, expensive labor, and harsh environmental conditions. There are many approaches crossing multiple scientific disciplines to better understand Antarctic processes; to evaluate ongoing climatic and environmental changes and their manifold ecological, physical, chemical, and geological consequences; and to make (improved) predictions. Together, these approaches generate very large, multivariate data sets, which can be broadly classified as “Antarctic big data”. For these large data sets, there is a pressing need for improved data acquisition, curation, integration, service, and application to support fundamental scientific research. Based on deficiencies in crossing disciplines and to attract further interest in big data in Antarctic sciences, this article will (i) describe and evaluate the current status of big data in various Antarctic-related scientific disciplines, (ii) identify current gaps, (iii) and provide solutions to fill these gaps.

GIMMI: Geographic Information and Mathematical Models Inter-operability

The article describes the contribution to the development of Kazakh linguistics and scientific thought based on the analysis of the scientific heritage of the outstanding figure of the early twentieth century Alash, scientist, physicist, mathematician, translator, talented linguist, one of the founders of Kazakh linguistics Eldes Omaruly. The scientific works of the scientist, written in the period of the 20-30s of the twentieth century, related to many areas of natural science and linguistics, such as history, translation, teaching methods and physics, mathematics, with a total volume of 5-6 volumes, are considered in detail and highlighted in them important problems and topical topics. As a result, the contribution of Еldes Omaruly to the development of Kazakh science, his merits in the formation of Kazakh linguistics will be evaluated. His linguistic heritage, works on phonetics, vocabulary, grammar and spelling, terminology, and onomastics are especially analyzed. Together with the teacher Akhmet Baitursynuly scientific continuity and original searches in the field of linguistics were compared, concrete conclusions were made about the contribution to the development of Kazakh writing and spelling. As a result of this analysis, scientific data from the scientist’s works on problematic issues of Kazakh spelling that arose on the eve of the alphabet reform today will be presented. The article is one of the first special research articles distinguished by innovative conclusions that contribute to the development of «eldesovedenie». The article mainly uses a descriptive method. The structural and comparative methods are partly used in the analysis of linguistic works.