Online tools supporting the conduct and reporting of systematic reviews and systematic maps: a case study on CADIMA and review of existing tools

Systematic reviews and systematic maps represent powerful tools to identify, collect, evaluate and summarise primary research pertinent to a specific research question or topic in a highly standardised and reproducible manner. Even though they are seen as the “gold standard” when synthesising primary research, systematic reviews and maps are typically resource-intensive and complex activities. Thus, managing the conduct and reporting of such reviews can become a time consuming and challenging task. This paper introduces the open access online tool CADIMA, which was developed through a collaboration between the Julius Kühn-Institut and the Collaboration for Environmental Evidence, in order to increase the efficiency of the evidence synthesis process and facilitate reporting of all activities to maximise methodological rigour. Furthermore, we analyse how CADIMA compares with other available tools by providing a comprehensive summary of existing software designed for the purposes of systematic review management. We show that CADIMA is the only available open access tool that is designed to: (1) assist throughout the systematic review/map process; (2) be suited to reviews broader than medical sciences; (3) allow for offline data extraction; and, (4) support working as a review team

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations [version 2; peer review: 2 approved]

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data

What Do We Know about the Chemistry of Strawberry Aroma?

The strawberry, with its unique aroma, is one of the most popular fruits worldwide. The demand for specific knowledge of metabolism in strawberries is increasing. This knowledge is applicable for genetic studies, plant breeding, resistance research, nutritional science, and the processing industry. The molecular basis of strawberry aroma has been studied for more than 80 years. Thus far, hundreds of volatile organic compounds (VOC) have been identified. The qualitative composition of the strawberry volatilome remains controversial though considerable progress has been made during the past several decades. Between 1997 and 2016, 25 significant analytical studies were published. Qualitative VOC data were harmonized and digitized. In total, 979 VOC were identified, 590 of which were found since 1997. However, 659 VOC (67%) were only listed once (single entries). Interestingly, none of the identified compounds were consistently reported in all of the studies analyzed. The present need of data exchange between “omic” technologies requires high quality and robust metabolic data. Such data are unavailable for the strawberry volatilome thus far. This review discusses the divergence of published data regarding both the biological material and the analytical methods. The VOC extraction method is an essential step that restricts interlaboratory comparability. Finally, standardization of sample preparation and data documentation are suggested to improve consistency for VOC quantification and measurement

Correction to: Online tools supporting the conduct and reporting of systematic reviews and systematic maps: a case study on CADIMA and review of existing tools

Abstract The authors wish to update information about the software DistillerSR in Tables 1 and 3 which we were alerted to following the publication of this article. In addition to the analysis provided, DistillerSR does support protocol development (Pi) e.g. assistance to determine appropriate PICO elements, and critical appraisal (Cr) as ‘stages of the SR process supported’. This information was not originally included in the assessment due to a lack of clarity on the service providers’ website. No further updates to this manuscript will be possible for this or other software, in line with the general disclaimer below. General disclaimer: The review of systematic review support software represents an independent assessment by EJ McIntosh based on publicly available information on each software package. This assessment represents an attempt to best capture information located via service providers’ websites, in academic publications, user manuals and via free trials or software demonstrations. Occasionally, relevant information was not publicly available or may have been difficult to access or interpret. This assessment does not represent the views or opinions of any of the software developers or service providers. The review of software was completed in mid-2017, readers should visit the software providers’ websites (linked in Table 1) to check for updates, for further information and to seek clarification where necessary

An automated field phenotyping pipeline for application in grapevine research

Due to its perennial nature and size, the acquisition of phenotypic data in grapevine research is almost exclusively restricted to the field and done by visual estimation. This kind of evaluation procedure is limited by time, cost and the subjectivity of records. As a consequence, objectivity, automation and more precision of phenotypic data evaluation are needed to increase the number of samples, manage grapevine repositories, enable genetic research of new phenotypic traits and, therefore, increase the efficiency in plant research. In the present study, an automated field phenotyping pipeline was setup and applied in a plot of genetic resources. The application of the PHENObot allows image acquisition from at least 250 individual grapevines per hour directly in the field without user interaction. Data management is handled by a database (IMAGEdata). The automatic image analysis tool BIVcolor (Berries in Vineyards-color) permitted the collection of precise phenotypic data of two important fruit traits, berry size and color, within a large set of plants. The application of the PHENObot represents an automated tool for high-throughput sampling of image data in the field. The automated analysis of these images facilitates the generation of objective and precise phenotypic data on a larger scale

MOESM1 of Online tools supporting the conduct and reporting of systematic reviews and systematic maps: a case study on CADIMA and review of existing tools

Additional file 1. Search strategy for identifying software programs listed in Table 1