Optimization of chemical plant simulation using double collocation

A method has been constructed for the solution of a wide range of chemical plant simulation models including differential equations and optimization. Double orthogonal collocation on finite elements is applied to convert the model into an NLP problem that is solved either by the VF 13AD package based on successive quadratic programming, or by the GRG2 package, based on the generalized reduced gradient method. This approach is termed simultaneous optimization and solution strategy. The objective functional can contain integral terms. The state and control variables can have time delays. Equalities and inequalities containing state and control variables can be included into the model as well as algebraic equations and inequalities. The maximum number of independent variables is 2. Problems containing 3 independent variables can be transformed into problems having 2 independent variables using finite differencing. The maximum number of NLP variables and constraints is 1500. The method is also suitable for solving ordinary and partial differential equations. The state functions are approximated by a linear combination of Lagrange interpolation polynomials. The control function can either be approximated by a linear combination of Lagrange interpolation polynomials or by a piecewise constant function over finite elements. The number of internal collocation points can vary by finite elements. The residual error is evaluated at arbitrarily chosen equidistant grid-points, thus enabling the user to check the accuracy of the solution between collocation points, where the solution is exact. The solution functions can be tabulated. There is an option to use control vector parameterization to solve optimization problems containing initial value ordinary differential equations. When there are many differential equations or the upper integration limit should be selected optimally then this approach should be used. The portability of the package has been addressed converting the package from V AX FORTRAN 77 into IBM PC FORTRAN 77 and into SUN SPARC 2000 FORTRAN 77. Computer runs have shown that the method can reproduce optimization problems published in the literature. The GRG2 and the VF I 3AD packages, integrated into the optimization package, proved to be robust and reliable. The package contains an executive module, a module performing control vector parameterization and 2 nonlinear problem solver modules, GRG2 and VF I 3AD. There is a stand-alone module that converts the differential-algebraic optimization problem into a nonlinear programming problem.

Psychophysiological responses to visceral and somatic pain in functional chest pain identify clinically relevant pain clusters

Background: Despite chronic pain being a feature of functional chest pain (FCP) its experience is variable. The factors responsible for this variability remain unresolved. We aimed to address these knowledge gaps, hypothesizing that the psychophysiological profiles of FCP patients will be distinct from healthy subjects. Methods: 20 Rome III defined FCP patients (nine males, mean age 38.7 years, range 28-59 years) and 20 healthy age-, sex-, and ethnicity-matched controls (nine males, mean 38.2 years, range 24-49) had anxiety, depression, and personality traits measured. Subjects had sympathetic and parasympathetic nervous system parameters measured at baseline and continuously thereafter. Subjects received standardized somatic (nail bed pressure) and visceral (esophageal balloon distension) stimuli to pain tolerance. Venous blood was sampled for cortisol at baseline, post somatic pain and post visceral pain. Key Results: Patients had higher neuroticism, state and trait anxiety, and depression scores but lower extroversion scores vs controls (all p < 0.005). Patients tolerated less somatic (p < 0.0001) and visceral stimulus (p = 0.009) and had a higher cortisol at baseline, and following pain (all p < 0.001). At baseline, patients had a higher sympathetic tone (p = 0.04), whereas in response to pain they increased their parasympathetic tone (p ≤ 0.008). The amalgamating the data, we identified two psychophysiologically distinct 'pain clusters'. Patients were overrepresented in the cluster characterized by high neuroticism, trait anxiety, baseline cortisol, pain hypersensitivity, and parasympathetic response to pain (all p < 0.03). Conclusions & Inferences: In future, such delineations in FCP populations may facilitate individualization of treatment based on psychophysiological profiling. © 2013 John Wiley & Sons Ltd.

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

Plant diversity influencing structure and functioning of Caatinga vegetation

O estudo dos efeitos que a diversidade de espécies pode causar nos processos ecossistêmicos tem crescido vertiginosamente nas últimas duas décadas. Diversos trabalhos experimentais realizados no mundo todo têm demonstrado que uma maior diversidade de plantas contribui para o aumento da produtividade de ecossistemas terrestres. Além disso, esse efeito pode influenciar processos em diversos níveis tróficos, contribuindo assim para a estabilidade dos processos ecossistêmicos a longo prazo. Paralelamente com os estudos do efeito da diversidade, muita atenção tem sido dada para desvendar o papel das características funcionais das espécies no funcionamento dos ecossistemas. Isto porque as características funcionais das espécies têm se mostrado importantes "peças" no entendimento dos efeitos que espécies individuais podem exercer nos ecossistemas e suas respostas ao ambiente. Nesta tese de doutorado eu explorei algumas lacunas de conhecimento dentro dessa área em crescente desenvolvimento conhecida na literatura ecológica como "biodiversidade e funcionamento dos ecossistemas". No primeiro capítulo, eu busquei evidências para mecanismos que podem explicar a relação positiva entre diversidade e funcionamento com foco em cinco mecanismos relacionados às interações entre plantas, tendo como parâmetro de funcionamento a produtividade primária. No segundo capítulo, eu utilizei técnicas para a estimativa de padrões de diversidade em escalas biogeográficas e bases de dados de satélites com longa duração para desvendar se a biodiversidade em escalas macroecológicas promove a estabilidade da produtividade dos ambientes terrestres no semiárido brasileiro. Por fim, o objetivo do terceiro capítulo foi entender como a perda da cobertura vegetal originária do uso da terra por comunidades tradicionais no semiárido brasileiro influenciaria os processos de interações entre plantas e o papel das características funcionais das espécies nessas interações. Acredito que a contribuição individual de cada capítulo preenche lacunas de conhecimento importantes dessa área da Ecologia que ainda se encontra em expansão.

Dissecting the Functional Impacts of Non-Coding Genetic Variation

A large proportion of the variation in traits between individuals can be attributed to variation in the nucleotide sequence of the genome. The most commonly studied traits in human genetics are related to disease and disease susceptibility. Although scientists have identified genetic causes for over 4,000 monogenic diseases, the underlying mechanisms of many highly prevalent multifactorial inheritance disorders such as diabetes, obesity, and cardiovascular disease remain largely unknown. Identifying genetic mechanisms for complex traits has been challenging because most of the variants are located outside of protein-coding regions, and determining the effects of such non-coding variants remains difficult. In this dissertation, I evaluate the hypothesis that such non-coding variants contribute to human traits and diseases by altering the regulation of genes rather than the sequence of those genes. I will specifically focus on studies to determine the functional impacts of genetic variation associated with two related complex traits: gestational hyperglycemia and fetal adiposity. At the genomic locus associated with maternal hyperglycemia, we found that genetic variation in regulatory elements altered the expression of the HKDC1 gene. Furthermore, we demonstrated that HKDC1 phosphorylates glucose in vitro and in vivo, thus demonstrating that HKDC1 is a fifth human hexokinase gene. At the fetal-adiposity associated locus, we identified variants that likely alter VEPH1 expression in preadipocytes during differentiation. To make such studies of regulatory variation high-throughput and routine, we developed POP-STARR, a novel high throughput reporter assay that can empirically measure the effects of regulatory variants directly from patient DNA. By combining targeted genome capture technologies with STARR-seq, we assayed thousands of haplotypes from 760 individuals in a single experiment. We subsequently used POP-STARR to identify three key features of regulatory variants: that regulatory variants typically have weak effects on gene expression; that the effects of regulatory variants are often coordinated with respect to disease-risk, suggesting a general mechanism by which the weak effects can together have phenotypic impact; and that nucleotide transversions have larger impacts on enhancer activity than transitions. Together, the findings presented here demonstrate successful strategies for determining the regulatory mechanisms underlying genetic associations with human traits and diseases, and value of doing so for driving novel biological discovery.

Functional application of lactic acid bacteria exopolysaccharide in complex food systems

Lactic acid bacteria expolysaccharides (LAB-EPS), in particular those formed from sucrose have the potential to improve food and beverage rheology and enhance their sensory properties potentially replacing or reducing expensive hydrocolloids currently used as improvers in food and beverage industries. Addition of sucrose not only enables EPS formation but also affects organic acid formation, thus influencing the sensory properties of the resulting food/beverage products. The first part of the study the organoleptic modulation of barley malt derived wort fermented using in situ produced bacterial polysaccharides has been investigated. Weisella cibaria MG1 was capable to produce exopolysaccharides during sucrosesupplemented barley malt derived wort fermentation. Even though the strain dominated the (sucrose-supplemented) wort fermentation, it was found to produce EPS (14.4 g l-1) with lower efficiency than in SucMRS (34.6 g l-1). Higher maltose concentration in wort led to the increased formation of oligosaccharide (OS) at the expense of EPS. Additionally, small amounts of organic acids were formed and ethanol remained below 0.5% (v/v). W. cibaria MG1 fermented worts supplemented with 5 or 10% sucrose displayed a shear-thinning behaviour indicating the formation of polymers. This report showed how novel and nutritious LAB fermented wort-base beverage with prospects for further advancements can be formulated using tailored microbial cultures. In the next step, the impact of exopolysaccharide-producing Weissella cibaria MG1 on the ability to improve rheological properties of fermented plant-based milk substitute plant based soy and quinoa grain was evaluated. W. cibaria MG1 grew well in soy milk, exceeding a cell count of log 8 cfu/g within 6 h of fermentation. The presence of W. cibaria MG1 led to a decrease in gelation and fermentation time. EPS isolated from soy yoghurts supplemented with sucrose were higher in molecular weight (1.1 x 108 g/mol vs 6.6 x 107 g/mol), and resulted in reduced gel stiffness (190 ± 2.89 Pa vs 244 ± 15.9 Pa). Soy yoghurts showed typical biopolymer gels structure and the network structure changed to larger pores and less cross-linking in the presence of sucrose and increasing molecular weight of the EPS. In situ investigation of Weissella cibaria MG1 producing EPS on quinoa-based milk was performed. The production of quinoa milk, starting from wholemeal quinoa flour, was optimised to maximise EPS production. On doing that, enzymatic destructuration of protein and carbohydrate components of quinoa milk was successfully achieved applying alpha-amylase and proteases treatments. Fermented wholemeal quinoa milk using Weissella cibaria MG1 showed high viable cell counts (>109 cfu/mL), a pH of 5.16, and significantly higher water holding capacity (WHC, 100 %), viscosity (> 0. 5 Pa s) and exopolysaccharide (EPS) amount (40 mg/L) than the chemically acidified control. High EPS (dextran) concentration in quinoa milk caused earlier aggregation because more EPS occupy more space, and the chenopodin were forced to interact with each other. Direct observation of microstructure in fermented quinoa milk indicated that the network structures of EPS-protein could improve the texture of fermented quinoa milk. Overall, Weissella cibaria MG1 showed favorable technology properties and great potential for further possible application in the development of high viscosity fermented quinoa milk. The last part of the study investigate the ex-situ LAB-EPS (dextran) application compared to other hydrocolloids as a novel food ingredient to compensate for low protein in biscuit and wholemeal wheat flour. Three hydrocolloids, xanthan gum, dextran and hydroxypropyl methylcellulose, were incorporated into bread recipes based on high-protein flours, low-protein flours and coarse wholemeal flour. Hydrocolloid levels of 0–5 % (flour basis) were used in bread recipes to test the water absorption. The quality parameters of dough (farinograph, extensograph, rheofermentometre) and bread (specific volume, crumb structure and staling profile) were determined. Results showed that xanthan had negative impact on the dough and bread quality characteristics. HPMC and dextran generally improved dough and bread quality and showed dosage dependence. Volume of low-protein flour breads were significantly improved by incorporation of 0.5 % of the latter two hydrocolloids. However, dextran outperformed HPMC regarding initial bread hardness and staling shelf life regardless the flour applied in the formulation.

Bioengineering strategies to improve functional qualities of nisin

The abuse of antibiotics and the emergence of multi-drug resistant bacterial strains have created the need to explore alternative methods of controlling microbial pathogens. The bacteriocin family of antimicrobial peptides has been proposed as one such alternative to classic antibiotics. Nisin A belongs to the subgroup of bacteriocins called the lantibiotics, which contain several unusual amino acids as a consequence of enzyme-mediated post-translational modifications. As nisin is produced by generally regarded as safe (GRAS) microorganisms, it could potentially be applied in a clinical setting. However, as lantibiotics are naturally produced in such small quantities, this can hinder their industrial potential. In order to overcome this, several approaches can be utilised. For example, given the gene encoded nature of lantibiotics, genetic engineering approaches can be implemented in order to yield variants with enhanced properties. Here, the use of mutagenesis-based strategies was employed to obtain a derivative of nisin with enhanced bioactivity in vitro. Investigations with purified peptide highlighted the enhanced specific activity of this variant, nisin M21V, against food-borne Listeria monocytogenes strains. Furthermore, this specific enhanced bioactivity was evident in a mouse model of listeriosis. Reductions in bioluminescence and microbial counts in organs from infected mice were observed following treatment with nisin M21V compared to that of wild-type nisin A. Peptide bioengineering approaches were also implemented to obtain additional novel derivatives of nisin. The generation of “S5X” and “S33X” banks (representing a change of natural serines at positions 5 and 33 to all possible alternative residues) by a combination of site-saturation and site-directed mutagenesis led to the identification of several derivatives exhibiting improved stability. This allowed the rational design of variants with enhanced stability compared to that of wild type nisin. Another means of tackling issues associated with lantibiotic yield is to combine lantibiotics with other antimicrobials. This could circumvent the need for enhanced production while also reducing concentrations of the peptide antimicrobials. We observed that combinations of nisin variants and low levels of plant essential oils (thymol, carvacrol, trans-cinnamaldehyde) significantly controlled Gram negative foodborne pathogens in in vitro assays compared to nisin A-essential oil combinations. This enhanced control was also evident in model food systems. Nisin variants used in conjunction with carvacrol significantly reduced numbers of E. coli O157:H7 in apple juice while a commercial nisin preparation used in combination with citric acid significantly controlled C. sakazakii in infant milk formula. It is noteworthy that while nisin is generally associated with Gram positive targets, upon combination with plant essential oils the spectrum of inhibition was broadened to Gram negative targets.

Aboveground plant community and species-specific vegetation cover from the Jena Experiment (Main Experiment, year 2009)

This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2009, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2009, in addition to the four community level cover estimates, cover of the moss layer was estimated.

Aboveground plant community and species-specific vegetation cover from the Jena Experiment (Main Experiment, year 2010)

This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2010, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

Aboveground plant community and species-specific vegetation cover from the Jena Experiment (Main Experiment, year 2013)

This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2013, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

Aboveground plant community and species-specific vegetation cover from the Jena Experiment (Main Experiment, year 2008)

This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2008, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

Soil temperature along a plant diversity gradient in the Jena Experiment (Main Experiment, 2013)

Soil temperature (in °C) was determined using a frequency domain sensor probe (WET-2 Sensor, Delta-T Devices, Cambridge, United Kingdom) on 1st August 2013. The device was inserted from the top 6 cm deep (length of the prongs) into the soil. The average of three measurements on the same day was calculated. All data where measured in the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown in the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, or 4 functional groups). Plots were maintained by bi-annual weeding and mowing.

Soil porosity along a plant diversity gradient in the Jena Experiment (Main Experiment, 2013)

Soil porosity is the fraction of total volume occupied by pores or voids measured at matric potential 0. To measure soil porosity, soil samples were taken from each plot using sample rings with an internal diameter of 57 mm and height of 40.5 mm (inner volume of Vs=100 cm3). The samples were placed on a sand bed box with water level set to allow saturation of the samples with water. After 48 h the samples were weighed (ms), oven dried at 105 °C and weighed again to determine the dry weight (md). We calculated soil porosity (n [%]) using the density of water (?w=1 g cm?3), n=100 ? (mw-md) / (?w?Vs). To account for the spatial variation of soil properties, three replicates were taken per plot, approximately 2, 3 and 4 weeks after the flood that occurred at the field site during June 2013. Data are the average soil porosity values per plot. All data where measured in the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown in the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, or 4 functional groups). Plots were maintained by bi-annual weeding and mowing.

Aboveground community and species-specific plant biomass from the Jena Experiment (Main Experiment, year 2009)

This data set contains aboveground community biomass in 2009 (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice in 2009 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the main experiment. This was done by clipping the vegetation at 3 cm above ground in three rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned prior to each harvest by random selection of coordinates within the core area of the plots (i.e. the central 10 x 15 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for all biomass measures are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship.

Aboveground community and species-specific plant biomass from the Jena Experiment (Main Experiment, year 2010)

This data set contains aboveground community biomass in 2010 (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice in 2010 just prior to mowing (during peak standing biomass in early June and in late August) on all experimental plots of the main experiment. This was done by clipping the vegetation at 3 cm above ground in two rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned prior to each harvest by random selection of coordinates within the core area of the plots (i.e. the central 10 x 15 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for all biomass measures are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship.