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This study aims to create a classifiable and comparable carbon footprint map of Turkey on the basis of cities with the provincial data inventory and data standard. Therefore, the basic parameters of carbon footprint change were determined by conducting the literature review. The carbon footprint change of each province was modeled using artificial neural networks for 2015 due to data availability. The performance metrics of the model created are (R2 0.97622, MSE 3.19809, RMSE 1.78832). The carbon footprint values of each province were predicted with the lowest error for the years 2014, 2015, 2016, and 2017, for which access data were available with the help of modeling. The spatial distributions of the prediction values were mapped using the geographic information system (GIS). An increase of 7 Mt on average was detected in the carbon footprint change of Turkey in 4 years. Furthermore, it was determined which parameter had a weighted effect in determining the carbon footprint values using the RReliefF algorithm, the feature selection method based on the established model’s prediction values. Accordingly, the parameters affecting the carbon footprint change on Turkey’s model were determined according to their weight as follows (income level 0.057, housing 0.029, the number of motor vehicles 0.028, sectoral industry size 0.025, population density 0.024, energy consumption 0.017, infrastructure 0.005, agricultural area 0.004, and agricultural production value 0.0008), respectively. This study draws a way for local governments to model their climate action plans with a standard data inventory, considering the parameters with weighted effects on carbon footprint change.

The explosive growth of geospatial data has stimulated the development of many standards aimed at decreasing data heterogeneity and enhancing data use. Well-established design methods for geospatial data standards typically involve the creation of two schemas for data structure, designated here as logical and physical, but this can lead to conceptual inconsistencies and modelling inefficiencies. In this paper we describe a design method that overcomes these issues by incorporating an additional schema – the conceptual schema – and demonstrate its application to the design of GroundWaterML2 (GWML2), a new international standard for groundwater data. Results include not only a new data standard, robustly constructed and tested, but also an enhanced method for geospatial data standard design.

Mass spectrometry (MS) imaging is a very active field of research, and has seen impressive progress in recent years [1, 2]. The number of groups that are working on this topic is constantly increasing. However, the field is still very heterogeneous in terms of applied instrumentation and data processing methods. In addition, complex datasets are reduced to a set of two-dimensional “images,” which inevitably results in information loss. This simplified graphical representation also strongly depends on processing options such as color scale, intensity normalization, and spatial interpolation. Consequently, experimental data are presented in very diverse ways, and published results can therefore be difficult to evaluate and compare. With a growing number of published studies, the issue of standardization and quality control of MS imaging data is becoming more important. This is a natural process for any new field that is maturing. The MS-based proteomics community has been facing similar issues in the last decade, and this discipline is therefore discussed as a “role model” herein. Since its inception in 2002, the Proteomics Standards Initiative (http://www.psidev.info) has driven the development of a number of minimum reporting guidelines (called “minimum information about a proteomics experiment” documents) [3] and several standard data formats for the different data types relevant in proteomics. For example, for raw and processed MS data, the data standard is called mzML [4]. In addition, several data repositories were established about 10 years ago to address the demand for storage and availability of MS data in the public domain [5–9]. A big step forward in this area has been the establishment of the ProteomeXchange (PX; http://www.proteomexchange.org/) consortium [10], led by the PRIDE [9] and PeptideAtlas [8] resources. The overall aim of PX is to provide a common framework and infrastructure for the cooperation of proteomics resources by defining and implementing consistent, harmonized, user-friendly data deposition and exchange procedures among the members. Thanks to the guidelines promoted by several scientific journals and funding agencies, and the general perception that sharing data is good scientific practice, the culture in the proteomics community has evolved toward data deposition as part of the publication process. In analogy to these activities in the MS proteomics field, similar mechanisms have been discussed and to some extent already implemented in the MS imaging community in recent years. A common data format for MS imaging—imzML—has been established [11]. This format is being used more and more, and the number of available tools is constantly growing (see http://www.imzml.org for more details). Reporting guidelines have been discussed for several years, and a first suggestion of those is included in this topical collection [12]. Nevertheless, owing to the lack of suitable resources, a missing element so far has been the possibility to make MS imaging datasets available in the public domain. Earlier attempts to develop a data repository were abandoned mainly because of the large size of MS imaging datasets. However, nowadays very large datasets (i.e., file size on the order of a few terabytes) can also be generated in MS-based proteomic and metabolomic studies, and can be submitted to established repositories. From a purely technical point of view, the infrastructure available in existing MS repositories is also suited for MS imaging data. Therefore, the missing step is to define and adopt a submission procedure in order to be compatible with MS-imaging-specific parameters. Here we describe the newly implemented way of submitting MS imaging data to PX via the PRIDE database. We also describe how to retrieve these data and to reproduce the MS images.

Background To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. Methods We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Results Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. Conclusions We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0367-z) contains supplementary material, which is available to authorized users.

The Clinical Data Interchange Standards Consortium (CDISC) has developed standards for data models, study design, and supporting clinical trial documents. CDISC standards have made such gains that the Center for Drug Evaluation and Research strongly encourages their use and implementation for submission of drug applications. However, despite the advances and improvements in these standards over the years and the ever-looming threat that they may someday be a requirement for drug applications, many are skeptical of a single standard and fail to understand the far-reaching advantages of adopting it. In this article we address the concerns and present numerous tangible benefits of CDISC standards.

With the ever increasing speed with which data are generated and the continual implementation of new instrumentation, maximizing the efficiency of data management for ligand-binding assays (LBAs) remains an increasing challenge. At the American Association of Pharmaceutical Scientists Workshop on the 21st Century Bioanalytical Laboratory: Maximizing Quality and Efficiency through Innovation, the attendees recognized the need to address the issue of data management. The eSolutions team, made up of end users and vendors, was formed with this challenge in mind, under the 21st Century Laboratory initiative. The eSolutions team has identified the need for a fully automated data interchange process as the first step to optimizing data management. This will require at least two major advances. First, for instruments capable of capturing raw data and metadata, a common open-source data standard is critical. Software independent of instruments will need to be compatible with the common data standard. Secondly, vendors must ensure that instruments and data analysis systems that process LBA data are enabled for direct bidirectional, file-less transfer of data between laboratory information management systems (LIMS), and instrument systems. In many laboratories conducting assays, LBA data systems and laboratory instruments are often islands of information, separated by an ocean of non-communication. Multiple applications within the laboratories generate reams of data that are stored in separate, non-connected silos of files. Data translation is required to share the data stored within these files with a LIMS or other analytical software. At best, this can be resolved using information technology (IT) resources. However, in the worst case scenario, this translation is performed manually leading to tedious error-prone tasks that require additional quality control (QC) oversight. In order to meet US FDA 21 CFR Part 11 (Electronic Records Electronic Signatures, ERES) regulations, data transfer through a file-based process requires assurance that the data imported into the LIMS are the same data exported from the data acquisition instrument. Therefore, one way to increase laboratory productivity would be through the use of a file-less automated process for data interchange tasks. Automation offers the performance of tasks in a secure, repeatable, and consistent manner without human intervention. Additional benefits of implementing an automated data interchange include: Facilitation of results and metadata transfer from source laboratory instruments (e.g., plate reader) to a data processing system (e.g., a LIMS) The ability to automate laboratory business processes where the format-consistent data can be programmed to be parsed and read by computer software Consistency of data formats between versions of the same software Data comparability between software applications even from different vendors avoiding the business risk of having to maintain legacy systems in order to retain the ability to read stored proprietary raw data in the future While many LBA laboratories clearly see a need for an automated data interchange process, these laboratories must raise awareness of this requirement by supporting the eSolutions team in establishing this process as well as supporting those vendors who choose to participate in the effort. It is absolutely necessary to have active vendor participation to be successful. There is a compelling reason for instrument and software vendors to participate in the creation of an automated data interchange process and to support its adoption. Innovative vendors of LIMS and analytical software will benefit from not having to keep up with the ever changing landscape of data formats. The automated data interchange process also allow new instrument vendors to more easily comply with data management requirements of LBA laboratories, thereby increasing the likelihood that innovative technologies will be adopted. On the other hand, LBA laboratories should be willing to accept an increase in cost for applications to account for costs incurred during adoption of the process and to patronize vendors that adopt the automated data interchange process. In this article, we provide perspectives on the benefit to the LBA community (vendors and users) and what is required to establish an automated data interchange process for LBA data.

The primary purpose of data standards is to improve the interoperability of data in an increasingly networked environment. Given the high cost of developing data standards, it is desirable to assess their quality. We develop a set of metrics and a framework for assessing data standard quality. The metrics include completeness, relevancy, and a combined measure. Standard quality can also be indirectly measured by assessing interoperability of data instances. We evaluate the framework on a data standard for financial reporting in United States, the Generally Accepted Accounting Principles (GAAP) Taxonomy encoded in eXtensible Business Reporting Language (XBRL), and the financial statements created using the standard by public companies. The results show that the data standard quality framework is useful and effective. Our analysis also reveals quality issues of the US GAAP XBRL taxonomy and provides useful feedback to taxonomy users. The Securities and Exchange Commission has mandated that all publicly listed companies must submit their filings using XBRL. Our findings are timely and have practical implications that will ultimately help improve the quality of financial data and the efficiency of the data supply chain in a networked business environment.

Due to lack of common data standards, the communicability and comparability of biological data across various levels of organization and taxonomic groups is continuously decreasing. However, the interdependence between molecular and higher levels of organization is of growing interest and calls for co-operations between biologists from different methodological and theoretical backgrounds. A general data standard in biology would greatly facilitate such co-operations. This article examines the role that defined and formalized vocabularies (i.e., ontologies) could have in developing such a data standard. I suggest basic criteria for developing data standards on grounds of distinguishing content, concept, nomenclatural, and format standards and discuss the role of data bases and their use of bio-ontologies in current activities for data standardization in biology. General principles of ontology development are introduced, including foundational ontology properties (e.g. class–subclass, parthood), and how concepts are defined. After addressing problems that are specific to morphological data, the notion of a general structure concept for morphology is introduced and why it is required for developing a morphological ontology. The necessity for a general morphological ontology to be taxon-independent and free of homology assumptions is discussed and how it can solve the problems of morphology. The article concludes with an outlook on how the use of ontologies will likely establish some sort of general data standard in biology and why the development of a set of commonly used foundational ontology properties and the use of globally unique identifiers for all classes defined in ontologies is crucial for its success.

The amount of data generated by NMR-based metabolomic experiments is increasing rapidly. Furthermore, diverse techniques increase the need for informative and comprehensive meta-data. These factors present a challenge in the dissemination, interpretation, reviewing and comparison of experimental results using this technology. Thus, there is a strong case for unification and standardisation of the data representation for both academia and industry. Here, a systems analysis of an NMR-based metabolomics experiment is presented in order to reveal the reporting requirements. An in-depth analysis of the NMR component of a metabolomics experiment has been produced, and a first round of data standard development completed. This has focussed on both one- and two-dimensional 1H NMR experiments, but is also applicable to higher dimensions and other nuclei. We also report the modelling of this schema using Unified Modelling Language (UML), and have extended this to a proof-of-concept implementation of the standard as an XML schema.

This article proposes a comprehensive set of data standards to address the submission of clinical data based on the work of the Clinical Data Interchange Standards Consortium (CDISC). The present components of the CDISC submission standards are described and related to Food and Drug Administration guidelines on submission as expressed in the 1999 guidance documents on regulatory submissions and the 2005 guidance on the electronic Common Technical Document (eCTD). The relative roles of the CDISC Study Data Tabulation Model (SDTM); the Case Report Tabulation Data Description Specification (defme.xml), which is based on the CDISC Operational Data Model (ODM); and the Analysis Dataset Models (ADaM) in meeting submission data requirements are next described. Finally, this article discusses implementation alternatives for sponsors, future directions, and the potential benefit of current projects to improve the submission process.

Contextual data collected concurrently with molecular samples are critical to the use of metagenomics in the fields of marine biodiversity, bioinformatics and biotechnology. We present here Marine Microbial Biodiversity, Bioinformatics and Biotechnology (M2B3) standards for “Reporting” and “Serving” data. The M2B3 Reporting Standard (1) describes minimal mandatory and recommended contextual information for a marine microbial sample obtained in the epipelagic zone, (2) includes meaningful information for researchers in the oceanographic, biodiversity and molecular disciplines, and (3) can easily be adopted by any marine laboratory with minimum sampling resources. The M2B3 Service Standard defines a software interface through which these data can be discovered and explored in data repositories. The M2B3 Standards were developed by the European project Micro B3, funded under 7th Framework Programme “Ocean of Tomorrow”, and were first used with the Ocean Sampling Day initiative. We believe that these standards have value in broader marine science

Electronic charts have come more and more into use for marine navigation. If complying with the relevant international standards they are an accepted substitute to the mandatory carriage of paper nautical charts. However, the potential of such official, high-quality electronic chart data goes far beyond the use for navigation. In order to show its potential, the basic concepts and features of official electronic charts as a marine geographic information data base are explained and the relation of Hydrographic Offices responsible for their production, to Maritime Administrations is described. The data standard to be followed for electronic charts opens the use of the data for a wide range of possible applications. If efficiently organised, Maritime Administrations can be provided with a powerful, versatile database establishing a single geographic reference data base for e.g. Vessel Traffic Services, for Search and Rescue as well as for various applications in Coastal Zone Management including coastal protection and marine environmental protection. Ultimately, digital marine geographic information will become part of the evolving global spatial data infrastructure.

There is value to patients, clinicians and researchers from having a single electronic health record data standard that allows an integrated view, including genotype and phenotype data. However, it is important that this integrated view of the data is not created through a single database because privacy breaches increase with the number of users, and such breaches are more likely with a single data warehouse. Furthermore, a single user interface should be avoided because each end user requires a different user interface. Finally, data sharing must be controlled by the patient, not the other end users of the data. A preferable alternative is a federated architecture, which allows data to be stored in multiple institutions and shared on a need-to-know basis. The data sharing raises questions of ownership and stewardship that require social and political answers, as well as consideration of the clinical and scientific benefits.