Who owns the data?

About ten years ago, a group of scientists began to argue that it was unfair to ask other scientists to pay to read the results of research that had been publicly funded.     

On the correct use of ‘data’

Acknowledgement  Thanks are due to Drs. Paul Johnston (P.Johnston@ exeter.ac.uk) and E. William Beese (ebeese@t-online.de) for kindly providing dictionary definitions and for sharing their thoughts about the correct use of ‘data’.     

TRUNCATULIX – a data warehouse for the legume community

Background  

Databases for either sequence, annotation, or microarray experiments data are extremely beneficial to the research community, as they centrally gather information from experiments performed by different scientists. However, data from different sources develop their full capacities only when combined. The idea of a data warehouse directly adresses this problem and solves it by integrating all required data into one single database – hence there are already many data warehouses available to genetics. For the model legume Medicago truncatula, there is currently no such single data warehouse that integrates all freely available gene sequences, the corresponding gene expression data, and annotation information. Thus, we created the data warehouse TRUNCATULIX, an integrative database of Medicago truncatula sequence and expression data.     

Can we integrate bioinformatics data on the Internet?

The NETTAB (Network Tools and Applications in Biology) 2001 Workshop entitled 'CORBA and XML: towards a bioinformatics-integrated network environment' was held at the Advanced Biotechnology Centre, Genoa, Italy, 17-18 May 2001.     

BioGPS and GXD: mouse gene expression data—the benefits and challenges of data integration

Mouse gene expression data are complex and voluminous. To maximize the utility of these data, they must be made readily accessible through databases, and those resources need to place the expression data in the larger biological context. Here we describe two community resources that approach these problems in different but complementary ways: BioGPS and the Mouse Gene Expression Database (GXD). BioGPS connects its large and homogeneous microarray gene expression reference data sets via plugins with a heterogeneous collection of external gene centric resources, thus casting a wide but loose net. GXD acquires different types of expression data from many sources and integrates these data tightly with other types of data in the Mouse Genome Informatics (MGI) resource, with a strong emphasis on consistency checks and manual curation. We describe and contrast the “loose” and “tight” data integration strategies employed by BioGPS and GXD, respectively, and discuss the challenges and benefits of data integration. BioGPS is freely available at http://biogps.org. GXD is freely available through the MGI web site (www.informatics.jax.org) or directly at www.informatics.jax.org/expression.shtml.     

Hammerhead redux: Does the new structure fit the old biochemical data?

The cleavage rates of 78 hammerhead ribozymes containing structurally conservative chemical modifications were collected from the literature and compared to the recently determined crystal structure of the Schistosoma mansoni hammerhead. With only a few exceptions, the biochemical data were consistent with the structure, indicating that the new structure closely resembles the transition state of the reaction. Since all the biochemical data were collected on minimal hammerheads that have a very different structure, the minimal hammerhead must be dynamic and occasionally adopt the quite different extended structure in order to cleave.     

Can monitoring data contribute to the biodiversity-ecosystem function debate? Evaluating data from a highly dynamic ecosystem

One of the most controversially discussed topics in current biodiversity-ecosystem function research is the transfer of results from experimental and theoretical studies to natural ecosystems. At the same time, monitoring data on biodiversity are requested as key indicators for the state of an ecosystem in most environmental evaluation frameworks. We analyse two monitoring data sets comprising information on abundance and biomass of macrozoobenthos communities in the German Wadden Sea in order to evaluate how much information monitoring data on biodiversity provide concerning ecosystem functioning and what implications this information (or the lack thereof) has for future monitoring programmes. Our results show a positive correlation between number of species of macrozoobenthos and its standing stock. Despite differences in overall biomass and individual size in different functional groups, this correlation remained consistent for different feeding guilds and therefore is likely to be independent of certain species traits. Moreover, functional turnover analyses indicate that increasing species richness is needed to maintain biomass levels over increasing periods of time. Whereas our data thus corroborate predictions from theory, we could not determine any causal relationships, because monitoring data commonly include only vague proxies for very few functional parameters, in our case standing biomass as a proxy for production. As to the use of diversity as an indicator for ecosystem functioning, we advise that management decisions are to be based on verified causal relationships and therefore strongly suggest the general incorporation of unambiguous proxies for functional parameters in the measuring campaigns of monitoring programmes.     

Combining data sharing with the master–worker paradigm in the common component architecture

Software component technologies are being accepted as an adequate solution for handling the complexity of applications. However, existing software component models tend to be specialized to some types of resource architectures (e.g. in-process, distributed environments, etc.) and/or do not provide a very high level of abstraction. This paper focuses on handling data sharing on operation invocations between components as a solution allowing applications to be efficiently executed on all kinds of resources. In particular, the data sharing pattern appears in master–worker applications, when workers need to access only a part of a large piece of data, either in read or write mode. This approach is applied to the Common Component Architecture model. Its benefits are discussed using an image rendering application.

Is DIA proteomics data FAIR? Current data sharing practices,available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.     

Estimating individual fitness in the wild using capture–recapture data

The concept of Darwinian fitness is central in evolutionary ecology, and its estimation has motivated the development of several approaches. However, measuring individual fitness remains challenging in empirical case studies in the wild. Measuring fitness requires a continuous monitoring of individuals from birth to death, which is very difficult to get in part because individuals may or may not be controlled at each reproductive event and recovered at death. Imperfect detection hampers keeping track of mortality and reproductive events over the whole lifetime of individuals. We propose a new statistical approach to estimate individual fitness while accounting for imperfect detection. Based on hidden process modelling of longitudinal data on marked animals, we show that standard metrics to quantify fitness, namely lifetime reproductive success, individual growth rate and lifetime individual contribution to population growth, can be extended to cope with imperfect detection inherent to most monitoring programs in the wild. We illustrate our approach using data collected on individual roe deer in an intensively monitored population.