From data to knowledge - big data needs stewardship, a plant phenomics perspective.

The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley.

EARLY FLOWERING 3 (ELF3) is an important regulator of various physiological and developmental processes and hence may serve to improve plant adaptation which will be essential for future plant breeding. To expand the limited knowledge on barley ELF3 in determining agronomic traits, we conducted field studies with heterogeneous inbred families (HIFs) derived from selected lines of the wild barley nested association mapping population HEB-25. During two growing seasons, phenotypes of nearly isogenic HIF sister lines, segregating for exotic and cultivated alleles at the ELF3 locus, were compared for 10 developmental and yield-related traits. We determine novel exotic ELF3 alleles and show that HIF lines, carrying the exotic ELF3 allele, accelerated plant development compared with the cultivated ELF3 allele, depending on the genetic background. Remarkably, the most extreme effects on phenology could be attributed to one exotic ELF3 allele differing from the cultivated Barke ELF3 allele in only one single nucleotide polymorphism (SNP). This SNP causes an amino acid substitution (W669G), which as predicted has an impact on the protein structure of ELF3. Consequently, it may affect phase separation behaviour and nano-compartment formation of ELF3 and, potentially, also its local cellular interactions causing significant trait differences between HIF sister lines.

Integration of high-throughput phenotyping with anatomical traits of leaves to help understanding lettuce acclimation to a changing environment.

MAIN CONCLUSION: The combination of image-based phenotyping with in-depth anatomical analysis allows for a thorough investigation of plant physiological plasticity in acclimation, which is driven by environmental conditions and mediated by anatomical traits. Understanding the ability of plants to respond to fluctuations in environmental conditions is critical to addressing climate change and unlocking the agricultural potential of crops both indoor and in the field. Recent studies have revealed that the degree of eco-physiological acclimation depends on leaf anatomical traits, which show stress-induced alterations during organogenesis. Indeed, it is still a matter of debate whether plant anatomy is the bottleneck for optimal plant physiology or vice versa. Here, we cultivated 'Salanova' lettuces in a phenotyping chamber under two different vapor pressure deficits (VPDs; low, high) and watering levels (well-watered, low-watered); then, plants underwent short-term changes in VPD. We aimed to combine high-throughput phenotyping with leaf anatomical analysis to evaluate their capability in detecting the early stress signals in lettuces and to highlight the different degrees of plants' eco-physiological acclimation to the change in VPD, as influenced by anatomical traits. The results demonstrate that well-watered plants under low VPD developed a morpho-anatomical structure in terms of mesophyll organization, stomatal and vein density, which more efficiently guided the acclimation to sudden changes in environmental conditions and which was not detected by image-based phenotyping alone. Therefore, we emphasized the need to complement high-throughput phenotyping with anatomical trait analysis to unveil crop acclimation mechanisms and predict possible physiological behaviors after sudden environmental fluctuations due to climate changes.

Opportunities and limits of controlled-environment plant phenotyping for climate response traits.

Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.

Enabling reusability of plant phenomic datasets with MIAPPE 1.1.

Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.

Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping.

Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome-wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker-trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non-parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage-specific growth affecting genes to elucidate important processes operating at different developmental phases.

Non-canonical structure, function and phylogeny of the Bsister MADS-box gene OsMADS30 of rice (Oryza sativa).

Bsister MADS-box genes play key roles in female reproductive organ and seed development throughout seed plants. This view is supported by their high conservation in terms of sequence, expression and function. In grasses, there are three subclades of Bsister genes: the OsMADS29-, the OsMADS30- and the OsMADS31-like genes. Here, we report on the evolution of the OsMADS30-like genes. Our analyses indicate that these genes evolved under relaxed purifying selection and are rather weakly expressed. OsMADS30, the representative of the OsMADS30-like genes from rice (Oryza sativa), shows strong sequence deviations in its 3' region when compared to orthologues from other grass species. We show that this is due to a 2.4-kbp insertion, possibly of a hitherto unknown helitron, which confers a heterologous C-terminal domain to OsMADS30. This putative helitron is not present in the OsMADS30 orthologues from closely related wild rice species, pointing to a relatively recent insertion event. Unlike other Bsister mutants O. sativa plants carrying a T-DNA insertion in the OsMADS30 gene do not show aberrant seed phenotypes, indicating that OsMADS30 likely does not have a canonical 'Bsister function'. However, imaging-based phenotyping of the T-DNA carrying plants revealed alterations in shoot size and architecture. We hypothesize that sequence deviations that accumulated during a period of relaxed selection in the gene lineage that led to OsMADS30 and the alteration of the C-terminal domain might have been a precondition for a potential neo-functionalization of OsMADS30 in O. sativa.

Multiscale metabolic modeling: dynamic flux balance analysis on a whole-plant scale.

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.

MetaCrop 2.0: managing and exploring information about crop plant metabolism.

MetaCrop is a manually curated repository of high-quality data about plant metabolism, providing different levels of detail from overview maps of primary metabolism to kinetic data of enzymes. It contains information about seven major crop plants with high agronomical importance and two model plants. MetaCrop is intended to support research aimed at the improvement of crops for both nutrition and industrial use. It can be accessed via web, web services and an add-on to the Vanted software. Here, we present several novel developments of the MetaCrop system and the extended database content. MetaCrop is now available in version 2.0 at http://metacrop.ipk-gatersleben.de.

Toward the identification and regulation of the Arabidopsis thaliana ABI3 regulon.

The plant-specific, B3 domain-containing transcription factor ABSCISIC ACID INSENSITIVE3 (ABI3) is an essential component of the regulatory network controlling the development and maturation of the Arabidopsis thaliana seed. Genome-wide chromatin immunoprecipitation (ChIP-chip), transcriptome analysis, quantitative reverse transcriptase-polymerase chain reaction and a transient promoter activation assay have been combined to identify a set of 98 ABI3 target genes. Most of these presumptive ABI3 targets require the presence of abscisic acid for their activation and are specifically expressed during seed maturation. ABI3 target promoters are enriched for G-box-like and RY-like elements. The general occurrence of these cis motifs in non-ABI3 target promoters suggests the existence of as yet unidentified regulatory signals, some of which may be associated with epigenetic control. Several members of the ABI3 regulon are also regulated by other transcription factors, including the seed-specific, B3 domain-containing FUS3 and LEC2. The data strengthen and extend the notion that ABI3 is essential for the protection of embryonic structures from desiccation and raise pertinent questions regarding the specificity of promoter recognition.

Elongation-related functions of LEAFY COTYLEDON1 during the development of Arabidopsis thaliana.

The transcription factor LEAFY COTYLEDON1 (LEC1) controls aspects of early embryogenesis and seed maturation in Arabidopsis thaliana. To identify components of the LEC1 regulon, transgenic plants were derived in which LEC1 expression was inducible by dexamethasone treatment. The cotyledon-like leaves and swollen root tips developed by these plants contained seed-storage compounds and resemble the phenotypes produced by increased auxin levels. In agreement with this, LEC1 was found to mediate up-regulation of the auxin synthesis gene YUCCA10. Auxin accumulated primarily in the elongation zone at the root-hypocotyl junction (collet). This accumulation correlates with hypocotyl growth, which is either inhibited in LEC1-induced embryonic seedlings or stimulated in the LEC1-induced long-hypocotyl phenotype, therefore resembling etiolated seedlings. Chromatin immunoprecipitation analysis revealed a number of phytohormone- and elongation-related genes among the putative LEC1 target genes. LEC1 appears to be an integrator of various regulatory events, involving the transcription factor itself as well as light and hormone signalling, especially during somatic and early zygotic embryogenesis. Furthermore, the data suggest non-embryonic functions for LEC1 during post-germinative etiolation.

Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation.

In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought.

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

Fully-automated root image analysis (faRIA).

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

Mutation of the ALBOSTRIANS Ohnologous Gene HvCMF3 Impairs Chloroplast Development and Thylakoid Architecture in Barley.

Gene pairs resulting from whole genome duplication (WGD), so-called ohnologous genes, are retained if at least one member of the pair undergoes neo- or sub-functionalization. Phylogenetic analyses of the ohnologous genes ALBOSTRIANS (HvAST/HvCMF7) and ALBOSTRIANS-LIKE (HvASL/HvCMF3) of barley (Hordeum vulgare) revealed them as members of a subfamily of genes coding for CCT motif (CONSTANS, CONSTANS-LIKE and TIMING OF CAB1) proteins characterized by a single CCT domain and a putative N-terminal chloroplast transit peptide. Recently, we showed that HvCMF7 is needed for chloroplast ribosome biogenesis. Here we demonstrate that mutations in HvCMF3 lead to seedlings delayed in development. They exhibit a yellowish/light green - xantha - phenotype and successively develop pale green leaves. Compared to wild type, plastids of mutant seedlings show a decreased PSII efficiency, impaired processing and reduced amounts of ribosomal RNAs; they contain less thylakoids and grana with a higher number of more loosely stacked thylakoid membranes. Site-directed mutagenesis of HvCMF3 identified a previously unknown functional domain, which is highly conserved within this subfamily of CCT domain containing proteins. HvCMF3:GFP fusion constructs were localized to plastids and nucleus. Hvcmf3Hvcmf7 double mutants exhibited a xantha-albino or albino phenotype depending on the strength of molecular lesion of the HvCMF7 allele. The chloroplast ribosome deficiency is discussed as the primary observed defect of the Hvcmf3 mutants. Based on our observations, the genes HvCMF3 and HvCMF7 have similar but not identical functions in chloroplast development of barley supporting our hypothesis of neo-/sub-functionalization between both ohnologous genes.

Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions.

Changes in climate are likely to have a negative impact on water availability and soil fertility in many maize-growing agricultural areas. The development of high-throughput phenotyping platforms provides a new prospect for dissecting the dynamic complex plant traits such as abiotic stress tolerance into simple components. The growth phenotypes of 20 maize (Zea mays L.) inbred lines were monitored in a non-invasive way under control, nitrogen, and water limitation as well as under combined nitrogen and water stress using an automated phenotyping system in greenhouse conditions. Thirteen biomass-related and morphophysiological traits were extracted from RGB images acquired at 33 time points covering developmental stages from leaf count 5 at the first imaging date to leaf count 10-13 at the final harvest. For these traits, genetic differences were identified and dynamic developmental trends during different maize growth stages were analyzed. The difference between control and water stress was detectable 3-10 days after the beginning of stress depending on the genotype, while the effect of limited nitrogen supply only induced subtle phenotypic effects. Phenotypic traits showed different response dynamics as well as multiple and changing interaction patterns with stress progression. The estimated biovolume, leaf area index, and color ratios were found to be stress-responsive at different stages of drought stress progression and thereby represent valuable reference indicators in the selection of drought-adaptive genotypes. Furthermore, genotypes could be grouped according to two typical growth dynamic patterns in water stress treatments by c-means clustering analysis. Inbred lines with high drought adaptability across time and development were identified and could serve as a basis for designing novel genotypes with desired, stage-specific growth phenotypes under water stress through pyramiding. Drought recovery potential may play an equal role as drought tolerance in plant drought adaptation.

The Arabidopsis AAC Proteins CIL and CIA2 Are Sub-functionalized Paralogs Involved in Chloroplast Development.

The Arabidopsis gene Chloroplast Import Apparatus 2 (CIA2) encodes a transcription factor that positively affects the activity of nuclear genes for chloroplast ribosomal proteins and chloroplast protein import machineries. CIA2-like (CIL) is the paralogous gene of CIA2. We generated a cil mutant by site-directed mutagenesis and compared it with cia2 and cia2cil double mutant. Phenotype of the cil mutant did not differ from the wild type under our growth conditions, except faster growth and earlier time to flowering. Compared to cia2, the cia2cil mutant showed more impaired chloroplast functions and reduced amounts of plastid ribosomal RNAs. In silico analyses predict for CIA2 and CIL a C-terminal CCT domain and an N-terminal chloroplast transit peptide (cTP). Chloroplast (and potentially nuclear) localization was previously shown for HvCMF3 and HvCMF7, the homologs of CIA2 and CIL in barley. We observed nuclear localization of CIL after transient expression in Arabidopsis protoplasts. Surprisingly, transformation of cia2 with HvCMF3, HvCMF7, or with a truncated CIA2 lacking the predicted cTP could partially rescue the pale-green phenotype of cia2. These data are discussed with respect to potentially overlapping functions between CIA2, CIL, and their barley homologs and to the function of the putative cTPs of CIA2 and CIL.

A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping.

BACKGROUND: Automated segmentation of large amount of image data is one of the major bottlenecks in high-throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmentation of unimodal plant images. To overcome the problem of ambiguous color information in unimodal data, images of different modalities can be combined to a virtual multispectral cube. However, due to motion artefacts caused by the relocation of plants between photochambers the alignment of multimodal images is often compromised by blurring artifacts. RESULTS: Here, we present an approach to automated segmentation of greenhouse plant images which is based on co-registration of fluorescence (FLU) and of visible light (VIS) camera images followed by subsequent separation of plant and marginal background regions using different species- and camera view-tailored classification models. Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of 93 % ( S D = 5 % ) using our two-step registration-classification approach. CONCLUSION: Automated segmentation of arbitrary greenhouse images exhibiting highly variable optical plant and background appearance represents a challenging task to data classification techniques that rely on detection of invariances. To overcome the limitation of unimodal image analysis, a two-step registration-classification approach to combined analysis of fluorescent and visible light images was developed. Our experimental results show that this algorithmic approach enables accurate segmentation of different FLU/VIS plant images suitable for application in fully automated high-throughput manner.

The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition.

BACKGROUND: The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. RESULTS: To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a "bring the infrastructure to the data" approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. CONCLUSION: The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.