349,746 research outputs found
Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences
The Ocean Biogeographic Information System (OBIS) is the world's most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the collection of biogeographic data (the presence of marine species in space and time) and operated with optimized data flows, quality control procedures and data standards specifically targeted to these data. Based on requirements from the growing OBIS community to manage datasets that combine biological, physical and chemical measurements, the OBIS-ENV-DATA pilot project was launched to develop a proposed standard and guidelines to make sure these combined datasets can stay together and are not, as is often the case, split and sent to different repositories. The proposal in this paper allows for the management of sampling methodology, animal tracking and telemetry data, biological measurements (e.g., body length, percent live cover, ...) as well as environmental measurements such as nutrient concentrations, sediment characteristics or other abiotic parameters measured during sampling to characterize the environment from which biogeographic data was collected. The recommended practice builds on the Darwin Core Archive (DwC-A) standard and on practices adopted by the Global Biodiversity Information Facility (GBIF). It consists of a DwC Event Core in combination with a DwC Occurrence Extension and a proposed enhancement to the DwC MeasurementOrFact Extension. This new structure enables the linkage of measurements or facts - quantitative and qualitative properties - to both sampling events and species occurrences, and includes additional fields for property standardization. We also embrace the use of the new parentEventID DwC term, which enables the creation of a sampling event hierarchy. We believe that the adoption of this recommended practice as a new data standard for managing and sharing biological and associated environmental datasets by IODE and the wider international scientific community would be key to improving the effectiveness of the knowledge base, and will enhance integration and management of critical data needed to understand ecological and biological processes in the ocean, and on land.Fil: De Pooter, Daphnis. Flanders Marine Institute; BélgicaFil: Appeltans, Ward. UNESCO-IOC; BélgicaFil: Bailly, Nicolas. Hellenic Centre for Marine Research, MedOBIS; GreciaFil: Bristol, Sky. United States Geological Survey; Estados UnidosFil: Deneudt, Klaas. Flanders Marine Institute; BélgicaFil: Eliezer, Menashè. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Fujioka, Ei. University Of Duke. Nicholas School Of Environment. Duke Marine Lab; Estados UnidosFil: Giorgetti, Alessandra. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Goldstein, Philip. University of Colorado Museum of Natural History, OBIS; Estados UnidosFil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Lipizer, Marina. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Mackay, Kevin. National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Marin, Maria Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Moncoiffé, Gwenaëlle. British Oceanographic Data Center; Reino UnidoFil: Nikolopoulou, Stamatina. Hellenic Centre for Marine Research, MedOBIS; GreciaFil: Provoost, Pieter. UNESCO-IOC; BélgicaFil: Rauch, Shannon. Woods Hole Oceanographic Institution; Estados UnidosFil: Roubicek, Andres. CSIRO Oceans and Atmosphere; AustraliaFil: Torres, Carlos. Universidad Autonoma de Baja California Sur; MéxicoFil: van de Putte, Anton. Royal Belgian Institute for Natural Sciences; BélgicaFil: Vandepitte, Leen. Flanders Marine Institute; BélgicaFil: Vanhoorne, Bart. Flanders Marine Institute; BélgicaFil: Vinci, Mateo. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; ItaliaFil: Wambiji, Nina. Kenya Marine and Fisheries Research Institute; KeniaFil: Watts, David. CSIRO Oceans and Atmosphere; AustraliaFil: Klein Salas, Eduardo. Universidad Simon Bolivar; VenezuelaFil: Hernandez, Francisco. Flanders Marine Institute; Bélgic
FVQA: Fact-based Visual Question Answering
Visual Question Answering (VQA) has attracted a lot of attention in both
Computer Vision and Natural Language Processing communities, not least because
it offers insight into the relationships between two important sources of
information. Current datasets, and the models built upon them, have focused on
questions which are answerable by direct analysis of the question and image
alone. The set of such questions that require no external information to answer
is interesting, but very limited. It excludes questions which require common
sense, or basic factual knowledge to answer, for example. Here we introduce
FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA
only contains questions which require external information to answer.
We thus extend a conventional visual question answering dataset, which
contains image-question-answerg triplets, through additional
image-question-answer-supporting fact tuples. The supporting fact is
represented as a structural triplet, such as .
We evaluate several baseline models on the FVQA dataset, and describe a novel
model which is capable of reasoning about an image on the basis of supporting
facts.Comment: 16 page
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
We present a new kind of question answering dataset, OpenBookQA, modeled
after open book exams for assessing human understanding of a subject. The open
book that comes with our questions is a set of 1329 elementary level science
facts. Roughly 6000 questions probe an understanding of these facts and their
application to novel situations. This requires combining an open book fact
(e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of
armor is made of metal) obtained from other sources. While existing QA datasets
over documents or knowledge bases, being generally self-contained, focus on
linguistic understanding, OpenBookQA probes a deeper understanding of both the
topic---in the context of common knowledge---and the language it is expressed
in. Human performance on OpenBookQA is close to 92%, but many state-of-the-art
pre-trained QA methods perform surprisingly poorly, worse than several simple
neural baselines we develop. Our oracle experiments designed to circumvent the
knowledge retrieval bottleneck demonstrate the value of both the open book and
additional facts. We leave it as a challenge to solve the retrieval problem in
this multi-hop setting and to close the large gap to human performance.Comment: Published as conference long paper at EMNLP 201
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