Modern life sciences research is depending on powerful IT-infrastructure. The process of gaining knowledge is tightly coupled with the data producer, bioinformatics tools, the structured data storage and long term archiving. In this context, Laboratory Information Management Systems (LIMS) are getting an increased focus in life sciences. Examples for LIMS implementations are presented in [1, 2] and [3]. The most common definition of a LIMS can be summarised as follows: LIMS is a computer software that is used in the laboratory for the management of samples, laboratory users, equipment, standards and other laboratory functions, such as invoicing, plate management and work flow automation. However, an important basis of all laboratory processes is the primary data. Primary data is read-only raw data that comes directly or indirectly from molecular biological analysis devices. The data must be available for all kinds of subsequent analysis and result interpretation (see Figure 1).
The data domain is manifold and ranges from DNA mapping and sequences, gene expression, proteomics to phenotyping. Over the past several decades, many of the life sciences have been transformed into high-throughput areas of study [4]. In a number of cases, the rate at which data can now be generated has increased by several orders of magnitude. Such dramatic expansions in data throughput have largely been enabled by engineering innovation, e.g. hardware advancements and automation. In particular, laboratory tasks that were once performed manually are now carried out by robotic fixtures. The growing trend towards automation continues to drive the urgent need for proper IT support [5]. Here, we use the acronym IT to name those infrastructure services that (a) process and analyse data, (b) organise and store data and provide structured data handling capability and (c) support the reporting, editing, arrangement and visualisation of data. The aim is to reflect the biologists data ⇒ information ⇒ knowledge paradigm [6] and to meet the requirements of high-throughput data analysis and the resulting data volume of petabytes [7].
The Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Germany, is a research centre, which primarily applies the concepts and technologies of modern biology to crop plants and hosts the German central ex situ gene bank. There are many scientific fields at the IPK producing high throughput data, such as plant phenotyping (http://www.lemnatec.com) and 454-sequencing [8]. One recurring task for bioinformatics at the IPK is the implementation of databases and information systems. To handle, manage and secure a huge amount of data properly, it is always a great advantage to have a well trained bioinformatics and IT staff which takes care for it and assumes responsibility for the valuable electronic information. We learned which design principles and implementation techniques are useful and which have to be avoided. Subsequently, we abstracted a best practice and proven implementation concept as experiences from several bioinformatics projects: SEMEDA (Semantic Meta Database) for providing semantically integrated access to databases and to edit and maintain ontologies and controlled vocabularies [9], MetaCrop, a manually curated repository of high quality information about the metabolism of crop plants [10] and an integration and analysis pipeline for systems biology in crop plant metabolism [11]. In this paper we summarise these experiences into best practice for primary data management. First, we will present the requirements of primary data management. Afterwards, we will give an overview to different solutions of primary data management. Furthermore, we will present our results and we will last but not least list our used methods.
The major aspects of primary data management
There are four major aspects about the handling of primary data. Today, automated techniques produces a large amount of data. It is necessary to find solutions for large storage capacity and efficient backup mechanism in a preferably affordable way. Especially during the life span of a project the data access has to be restricted to project related users. Finding a way to manage the access rights for users and even groups within the primary data management will be another important aspect. One most important fact about primary data management is the performance of storing files and an efficient retrieval of the stored data. The third main aspect would be to realise such an efficient system. The last important aspect is the usability of the system. The user interface has to be simple and intuitive for the different biological groups using the system.
The central requirements of primary data management
Figure 2 illustrates the architecture that has to be realised for a primary lab data management system. There have to be components for upload and retrieval of this data. Next, the data has to be secured so that only authorised users can access specific data. The last important fact is the storage. The data has to be stored efficiently and the metadata has to be available for data retrieval.
Referring to the four major aspects of primary data management and the architecture in Figure 2 there can be derived several requirements for managing high throughput primary lab data:
Best practices for the above listed requirements are presented in the following sections.
Solutions for primary data management
A major aspect in scientific research is the citation of data for publications. Anderson et al. [12] show, that there is a increasing number of publications containing supplementary material and that data has to be long-term archived. But often data is not longer available, especially for elder publications. To make data citable a persistent identifier linked with the data is needed. Such identifier is provided by DOI (Digital Object Identifier, http://www.doi.org). One system working with DOIs is the Pangaea system (http://www.pangaea.de/), a publishing network for geoscientific and environment data. It uses TIB, the German National Library of science and technology, which provides services for DOI registration. TIB is a member of DataCite, an organisation with currently 15 members and 4 associate members, which has the main goal to guarantee easy access to scientific data (http://www.datacite.org). Another motivation is shown by Piwowar et al. [13]. There is a correlation between the publication of research data and the number of citations. Publications with shared data are more often cited than publications without any publicly available data.
The creation of central data repositories is a further task of scientific research. One example of such a repository is GabiPD [14]. GabiPD stores data from high throughput experiments of different plant species and further provides methods to analyse and visualise the stored data. The online archive PsychData [15] is an open access central repository which provides long term documented data over all aspects of psychology. With this archive the scientific community has a platform to store and to exchange their data with other scientists, which leads to a better collaborative scientific usability of the data. Another system to store scientific data is the LabKey Server [16]. The main advantage of this platform is the ability to integrate, analyse and visualise data from diverse data sources, which is very useful in large research consortia. For digital long term storage, in Germany the nestor organisation was founded (http://www.longtermpreservation.de). It provides a guideline of standardisation activities regarding persistent identifiers, metadata, file formats, certification, reference models and records management. Nestor is also partner in the European community APA (alliance for permanent access, http://www.alliancepermanentaccess.org/).
