# Data management

The topic data management includes methodical standards to structure and quality assure research data as recorded by standardized methods. Quality by supervised or unsupervised procedures e.g. data completeness, normal distribution, integrity, and removal of outliers, is prerequisite for wellmaintained data storage, -exchange, -processing and -evaluation. Descriptive data should be checked on misspellings, synonyms and inconsistencies to enable clear data allocation and combination of different data sets.

When research data are collected, tested, described, and maybe pre-processed (e.g. pedotransferfunctions, biological models, upscaling) data must be technically prepared to be transferred into the database. At this point internal database management becomes relevant. Data (base) management includes e.g. rules on the data structure, languages and formats used. These need to meet the requirements on later data applications such as archiving, evaluation, reuse, and publishing. Research data management is widely implemented within research institutes in so-called Current Research Information Systems (CRIS) and in national or international (inter-)disciplinary data repositories. An example for national agricultural data management is the PIAF system (Planning, Information and Analysis for Field trials). An international and interdisciplinary open data repository for the wide field of environmental science is the PANGAEA.

Internationally accepted Data Management Plans (DMP) and open research data portals may help to plan, manage and publish research data. Tools and guidelines are provided by open access platforms and repositories such as ReDBox (Australia), OpenAIRE, CGIAR, EUDAT, EOSC, and INSPIRE.

This chapter provides an overview of standards with general requirements on data quality, -structure -formats, and –types as well as geographic reference systems, units and dimensions.