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Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated—even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy‐to‐read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights. [ABSTRACT FROM AUTHOR]

Daiquiri is a web‐based user interface (UI) framework for control system monitoring and data acquisition designed for synchrotron beamlines. It provides simple, intuitive and responsive interfaces to control and monitor hardware, launch acquisition sequences and manage associated metadata. Daiquiri concerns itself only with the UI layer; it does not provide a scan engine or controls system but can be easily integrated with existing systems. [ABSTRACT FROM AUTHOR]

Freshwater conservation is vital to the maintenance of global biodiversity. Ponds are a critical, yet often under‐recognized, part of this, contributing to overall ecosystem functioning and diversity. They provide habitats for a range of aquatic, terrestrial, and amphibious life, often including rare and declining species.Effective, rapid, and accessible survey methods are needed to enable evidence‐based conservation action, but freshwater taxa are often viewed as "difficult"—and few specialist surveyors are available. Datasets on ponds are therefore limited in their spatiotemporal coverage.With the advent of new recording technologies, acoustic survey methods are becoming increasingly available to researchers, citizen scientists, and conservation practitioners. They can be an effective and noninvasive approach for gathering data on target species, assemblages, and environmental variables. However, freshwater applications are lagging behind those in terrestrial and marine spheres, and as an emergent method, research studies have employed a multitude of different sampling protocols.We propose the Pond Acoustic Sampling Scheme (PASS), a simple protocol to allow a standardized minimal sample to be collected rapidly from small waterbodies, alongside environmental and methodological metadata. This sampling scheme can be incorporated into a variety of survey designs and is intended to allow access to a wide range of participants, without requiring complicated or prohibitively expensive equipment.Adoption of this sampling protocol would enable consistent sound recordings to be gathered by researchers and conservation organizations, and allow the development of landscape‐scale surveys, data sharing, and collaboration within an expanding freshwater ecoacoustic community—rather than individual approaches that produce incompatible datasets. The compilation of standardized data would improve the prospects for effective research into the soundscapes of small waterbodies and aid freshwater conservation efforts. [ABSTRACT FROM AUTHOR]

Improving technology and increasing affordability mean that camera trapping—the use of remotely triggered cameras to photograph wildlife—is becoming an increasingly common tool in the monitoring and conservation of wild populations. Each camera trap study generates a vast amount of data, which need to be processed and labeled before analysis. Traditionally, processing camera trap data has been performed manually by entering data into a spreadsheet. This is time‐consuming, prone to human error, and data management may be inconsistent between projects, hindering collaboration. Recently, several programs have become available to facilitate and quicken data processing. Here, we review available software and assess their ability to better standardize camera trap data management and facilitate data sharing and collaboration. To identify available software for camera trap data management, we used internet searches and contacted researchers and practitioners working on large camera trap projects, as well as software developers. We tested all available programs against a range of software characteristics in addition to their ability to record a suite of important data variables extracted from images. We identified and reviewed 12 available programs for the management of camera trap data. These ranged from simple software assisting with the extraction of metadata from an image, through to comprehensive programs that facilitate data entry and analysis. Many of the programs tested were developed for use on specific studies and so do not cover all possible software or data collection requirements that different projects may have. We highlight the importance of a standardized software solution for camera trap data management. This approach would allow all possible data to be collected, enabling researchers to share data and contribute to other studies, as well as facilitating multi‐project comparisons. By standardizing camera trap data collection and management in this way, future studies would be better placed to guide conservation policy on a global level. The use of camera traps in ecology and conservation is increasingly common, and studies using camera traps can generate masses of data. These data are often handled and stored differently, hindering comparison and collaboration. We reviewed 12 camera trap data management programs and advise that that a universal approach to managing, storing, and analzsing camera trap data will facilitate collaborations and the sharing of data and will be beneficial to global conservation. [ABSTRACT FROM AUTHOR]

Premise: Heterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources. Methods: In 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek). Results: Over 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals. Discussion: We highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data. [ABSTRACT FROM AUTHOR]

Open government data are about wide and free availability of public information created or collected by public entities. The International Open Data Charter and Findable, Accessible, Interoperable And Reusable Data (FAIR) data principles were selected as the guiding principles for the development of this report. A review of open data maturity reports indicated that most of the EU28+ are making significant progress in open government data, however there are different levels of adoption in food and feed safety organisations. EFSA's Scientific Data Warehouse (SDWH) access rules were defined in 2015 (EFSA, 2015a); this report extends them to an open‐by‐default approach. Precautions are specified to ensure people, products or food businesses are not identified, and that information deemed confidential is protected. A proactive scientific data publication process that is open, timely, comprehensive, comparable, interoperable, accessible and usable is defined. Publication can be performed on behalf of data providers by EFSA (Supported publications) or by organisations according to their own open data policy (self‐publishers). Structural metadata to support interoperability and comply with data protection requirements is defined as are the minimum descriptive and administrative metadata to support accessibility and usability. Datasets will be published in the open evidence community Knowledge Junction and assigned a Digital Object Identifier to ensure persistence and allow tracking of reuse. API technology and Data Catalogue Vocabulary Application Profile (DCAT‐AP) will be used to ensure integration with the EU open data ecosystem. Attribution and citation to give credit to parties that have contributed to the creation of datasets are emphasised. The working group identified many benefits of open data and proposes mechanisms to overcome perceived barriers. Upon endorsement, EFSA should proceed with the scheduling of the publication of data from the SDWH. New data collections with a requirement to store data in the SDWH would be subject to this open data approach. [ABSTRACT FROM AUTHOR]