Building a Global Ecosystem Research Infrastructure to Address Global Grand Challenges for Macrosystem Ecology

The development of several large-, "continental"-scale ecosystem research infrastructures over recent decades has provided a unique opportunity in the history of ecological science. The Global Ecosystem Research Infrastructure (GERI) is an integrated network of analogous, but independent, site-based ecosystem research infrastructures (ERI) dedicated to better understand the function and change of indicator ecosystems across global biomes. Bringing together these ERIs, harmonizing their respective data and reducing uncertainties enables broader cross-continental ecological research. It will also enhance the research community capabilities to address current and anticipate future global scale ecological challenges. Moreover, increasing the international capabilities of these ERIs goes beyond their original design intent, and is an unexpected added value of these large national investments. Here, we identify specific global grand challenge areas and research trends to advance the ecological frontiers across continents that can be addressed through the federation of these cross-continental-scale ERIs.Peer reviewe

Symposium & Panel Discussion: Data Citation and Attribution for Reproducible Research in Linguistics

Slides from the symposium and panel discussion at the event "Data Citation and Attribution for Reproducible Research in Linguistics," Annual Meeting of the Linguistic Society of America, Austin, TX, 5 January 2017.This material is based upon work supported by the National Science Foundation under grant SMA-1447886

Ocean FAIR Data Services

Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and application by current and future users. Effective data management requires collaboration across activities including observations, metadata and data assembly, quality assurance and control (QA/QC), and data publication that enables local and interoperable discovery and access and secures archiving that guarantees long-term preservation. To achieve this, data should be findable, accessible, interoperable, and reusable (FAIR). Here, we outline how these principles apply to ocean data and illustrate them with a few examples. In recent decades, ocean data managers, in close collaboration with international organizations, have played an active role in the improvement of environmental data standardization, accessibility, and interoperability through different projects, enhancing access to observation data at all stages of the data life cycle and fostering the development of integrated services targeted to research, regulatory, and operational users. As ocean observing systems evolve and an increasing number of autonomous platforms and sensors are deployed, the volume and variety of data increase dramatically. For instance, there are more than 70 data catalogs that contain metadata records for the polar oceans, a situation that makes comprehensive data discovery beyond the capacity of most researchers. To better serve research, operational, and commercial users, more efficient turnaround of quality data in known formats and made available through Web services is necessary. In particular, automation of data workflows will be critical to reduce friction throughout the data value chain. Adhering to the FAIR principles with free, timely, and unrestricted access to ocean observation data is beneficial for the originators, has obvious benefits for users, and is an essential foundation for the development of new services made possible with big data technologies

Recap of Workshop 1

A recap of the first workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, which was held at the University of Colorado in September 2015. Presented at the second workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, held at the University of Texas, April 8-10, 2016.National Science Foundation (NSF-SMA 1447886

Collecting and Preserving Local and Traditional Climate Knowledge

<p>The Exchange for Local Observations and Knowledge of the Arctic (ELOKA) facilitates the collection, preservation, exchange, and use of local observations and knowledge of the Arctic. Local and Traditional Knowledge (LTK) provides rich information about the Arctic that complements data acquired via conventional quantitative data collection methods. ELOKA seeks to make LTK and community observations discoverable and accessible to community members, scientists, educators, policy makers, and the general public. </p

01 - Welcome

Outlines the goals of this NSF project, explores the issue of reproducibility in science and linguistics, and explains the distinction between and importance of citation and attribution. Presented at the first workshop on Developing Standards for Data Citation and Attribution for Reproducible Research in Linguistics, held at the University of Colorado at Boulder from September 18-20 2015.This material is based upon work supported by the National Science Foundation under grant SMA-1447886

Collecting and Preserving Local and Traditional Climate Knowledge

<p>The Exchange for Local Observations and Knowledge of the Arctic (ELOKA) facilitates the collection, preservation, exchange, and use of local observations and knowledge of the Arctic. Local and Traditional Knowledge (LTK) provides rich information about the Arctic that complements data acquired via conventional quantitative data collection methods. ELOKA seeks to make LTK and community observations discoverable and accessible to community members, scientists, educators, policy makers, and the general public. </p

Today's Data are Part of Tomorrow's Research: Archival Issues in the Sciences

Scientific data are essential for training in science and informed decision-making regarding health, the environment, and the economy. Cumulative data sets assist with understanding trends, frequencies and patterns, and can form a baseline upon which we can develop predictions. This paper discusses the preservation of scientific data, providing an overview of the characteristics of scientific data and scientific-data portals from a variety of fields, with a focus on data quality, particularly accuracy, reliability and authenticity, and how these are captured in metadata. These concepts are broadly defined from both scientific and archival perspectives. Based on an extensive literature review of publications from national and international scientific organizations, government and research funding bodies, and empirical evidence from a selection of InterPARES 2 Case Studies and General Study 10, which investigated thirty-two scientificdata portals, the paper includes a brief examination of machine-base “knowledge representation” (KR) and the potential implications for the preservation of scientific data, with a particular focus on formal ontologies. The paper also discusses the concept of record in the context of Web 2.0 environments, the paucity of scientific data archives, and the lack of funding priorities in this area. It is argued that archivists will have to work closely with scientific-data creators to understand their practices, that data portals are mechanisms that archivists can use to extend their preservation practices, and that it is not technology that is impeding progress regarding the preservation of scientific data; it is a lack of funding, policy, prioritizing, and vision allowing our scientific national resources to be lost. RÉSUMÉ Les données scientifiques sont essentielles à la formation en sciences et à la prise de décision éclairée au sujet de la santé, de l’environnement et de l’économie. Les ensembles de données cumulatives aident à comprendre les tendances, les fréquences et les courants, et ils peuvent servir de base pour développer des prévisions. Cet article se penche sur la préservation des données scientifiques et des portails de données scientifiques d’un ensemble de domaines, en ciblant la qualité des données – surtout l’exactitude, la fiabilité et l’authenticité – et en examinant comment ces caractéristiques sont saisies par les métadonnées. Les auteurs donnent des définitions générales de ces concepts, dans des perspectives à la fois scientifiques et archivistiques. À partir d’une recension approfondie de la littérature sur le sujet (publications provenant d’organisations scientifiques nationales et internationales, d’organismes gouvernementaux et d’organismes de financement, ainsi que des observations empiriques d’un échantillon d’études de cas d’InterPARES 2 et de « General Study 10 » qui étudiaient 32 portails de données scientifiques), cet article examine sommairement la « représentation des connaissances » électronique (« machine-base “knowledge representation” [KR] ») et les répercussions possibles sur la préservation des données scientifiques, avec un accent particulier sur les ontologies formelles. Il présente aussi le concept de document dans le contexte d’un environnement Web 2.0, la rareté des archives sur les données scientifiques, et le fait que ce domaine ne figure pas souvent dans les priorités de financement. Les auteurs avancent que les archivistes devront travailler de près avec les scientifiques créateurs de données afin de comprendre leurs pratiques; que les portails de données sont des mécanismes dont les archivistes peuvent se servir pour parfaire leurs pratiques de préservation; et que ce n’est pas la technologie qui empêche le progrès en ce qui concerne les données scientifiques. C’est plutôt le manque de ressources, de politiques, de classement par ordre de priorités, et de vision qui occasionne la perte de nos ressources scientifiques nationales