The Scientific Drilling Database (SDDB) - Data from Deep Earth Monitoring and Sounding

Projects in the International Scientific Continental Drilling Program (ICDP) produce large amounts of data. Since the start of ICDP, data sharing has played an important part in ICDP projects, and the ICDP Operational Support Group, which provides the infrastructure for data capturing for many ICDP projects, has facilitated dissemination of data within project groups. However, unless published in journalpapers or books the data themselves in most cases were not available outside of the respective projects (see Conze et al. 2007, p. 32 this issue). With the online Scientific Drilling Database (SDDB; http://www.scientificdrilling.org), ICDP and GeoForschungsZentrum Potsdam (GFZ), Germany created a platform for the public dissemination of drilling data

An Open Source Web Service For Registering And Managing Environmental Samples

Records of environmental samples, such as minerals, soil, rocks, water, air and plants, are distributed across legacy databases, spreadsheets or other proprietary data systems. Sharing and integration of the sample records across the Web requires globally unique identifiers. These identifiers are essential in order to locate samples unambiguously and to manage their associated metadata and data systematically. The International Geo Sample Number (IGSN) is a persistent, globally unique label for identifying environmental samples. IGSN can be resolved to a digital representation of the sample through the Handle system. IGSN names are registered by end-users through allocating agents, which are the institutions acting on behalf of the IGSN registration agency. As an IGSN allocating agent, our goal is to implement a web service based on existing open source tools to streamline the processes of registering IGSNs and for managing and disseminating sample metadata. In this paper, we present our ongoing work on the design and development of the web service, and its data schema and database model for capturing key aspects of environmental samples. We show how existing controlled vocabularies can be incorporated into the service development to support the metadata registration of different types of samples. The proposed sample registration and curating approach has been trialed in the context of the Capricorn Distal Footprints project on a range of different sample types, varying from water to hard rock samples. The initial results demonstrate the effectiveness of the service while maintaining the flexibility to adapt to various media types, which is critical in the context of a multi-disciplinary project

Assembly and concept of a web-based GIS within the paleolimnological project CONTINENT (Lake Baikal, Russia)

Web-based Geographical Information Systems (GIS) are excellent tools within interdisciplinary and multi-national geoscience projects to exchange and visualize project data. The web-based GIS presented in this paper was designed for the paleolimnological project 'High-resolution CONTINENTal paleoclimate record in Lake Baikal' (CONTINENT) (Lake Baikal, Siberia, Russia) to allow the interactive handling of spatial data. The GIS database combines project data (core positions, sample positions, thematic maps) with auxiliary spatial data sets that were downloaded from freely available data sources on the world wide web. The reliability of the external data was evaluated and suitable new spatial datasets were processed according to the scientific questions of the project. GIS analysis of the data was used to assist studies on sediment provenance in Lake Baikal, or to help answer questions such as whether the visualization of present-day vegetation distribution and pollen distribution supports the conclusions derived from palynological analyses. The refined geodata are returned back to the scientific community by using online data publication portals. Data were made citeable by assigning persistent identifiers (DOI) and were published through the German National Library for Science and Technology (TIB Hannover, Hannover, Germany).Continen

Langzeitarchivierung von Forschungsdaten : eine Bestandsaufnahme

The relevance of research data today and for the future is well documented and discussed, in Germany as well as internationally. Ensuring that research data are accessible, sharable, and re-usable over time is increasingly becoming an essential task for researchers and research infrastructure institutions. Some reasons for this development include the following: - research data are documented and could therefore be validated - research data could be the basis for new research questions - research data could be re-analyzed by using innovative digital methods - research data could be used by other disciplines Therefore, it is essential that research data are curated, which means they are kept accessible and interpretable over time. In Germany, a baseline study was undertaken analyzing the situation in eleven research disciplines in 2012. The results were then published in a German-language edition. To address an international audience, the German-language edition of the study has been translated and abridged

Updating the Data Curation Continuum

The Data Curation Continuum was developed as a way of thinking about data repository infrastructure. Since its original development over a decade ago, a number of things have changed in the data infrastructure domain. This paper revisits the thinking behind the original data curation continuum and updates it to respond to changes in research objects, storage models, and the repository landscape in general. &nbsp

Versioning data is about more than revisions : A conceptual framework and proposed principles

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.Peer reviewe

Making Research Data Repositories Visible: The re3data.org Registry

Researchers require infrastructures that ensure a maximum of accessibility, stability and reliability to facilitate working with and sharing of research data. Such infrastructures are being increasingly summarized under the term Research Data Repositories (RDR). The project re3data.org-Registry of Research Data Repositories-has begun to index research data repositories in 2012 and offers researchers, funding organizations, libraries and publishers an overview of the heterogeneous research data repository landscape. In July 2013 re3data.org lists 400 research data repositories and counting. 288 of these are described in detail using the re3data.org vocabulary. Information icons help researchers to easily identify an adequate repository for the storage and reuse of their data. This article describes the heterogeneous RDR landscape and presents a typology of institutional, disciplinary, multidisciplinary and project-specific RDR. Further the article outlines the features of re3data.org, and shows how this registry helps to identify appropriate repositories for storage and search of research data

Raritas: a program for counting high diversity categorical data with highly unequal abundances

Acquiring data on the occurrences of many types of difficult to identify objects are often still made by human observation, for example, in biodiversity and paleontologic research. Existing computer counting programs used to record such data have various limitations, including inflexibility and cost. We describe a new open-source program for this purpose—Raritas. Raritas is written in Python and can be run as a standalone app for recent versions of either MacOS or Windows, or from the command line as easily customized source code. The program explicitly supports a rare category count mode which makes it easier to collect quantitative data on rare categories, for example, rare species which are important in biodiversity surveys. Lastly, we describe the file format used by Raritas and propose it as a standard for storing geologic biodiversity data. ‘Stratigraphic occurrence data’ file format combines extensive sample metadata and a flexible structure for recording occurrence data of species or other categories in a series of samples

FAIRer Data through Digital Recording: The FAIMS Mobile Experience

Critical data and metadata must be captured or created in the field, or shortly thereafter, to avoid loss. For the past 10 years, the Field Acquired Information Management Systems (FAIMS) project has developed and operated a customisable field data capture platform. Over time, we built features and approaches that incorporated the Findable, Accessible, Interoperable, and Reusable (FAIR) principles into born-digital datasets created during fieldwork. This paper synthesises our experience helping more than 40 projects adapt the FAIMS platform to nearly 70 research workflows in archaeology and other fieldwork domains. We review what elements of the FAIR Data Principles FAIMS was able to build into our software, how users received these capabilities, and what sociotechnical challenges impeded creation of FAIRer field data. Based on our experience, we argue that field data capture software can facilitate the production of FAIRer data, making those data much more Findable and Reusable, and somewhat more Accessible and Interoperable. Any such improvements, however, depend upon (1) making FAIR-data features an integral part of field data collection systems, minimising the burden imposed on researchers, and (2) researchers’ willingness to spend time and resources implementing FAIR Data Principles that do not provide immediate benefits to their research