Suchergebnisse

1. As systems of marine protected areas (MPAs) expand globally, there is a risk that new MPAs will be biased toward places that are remote or unpromising for extractive activities, and hence follow the trend of terrestrial protected areas in being 'residual' to commercial uses. Such locations typically provide little protection to the species and ecosystems that are most exposed to threatening processes. 2. There are strong political motivations to establish residual reserves that minimize costs and conflicts with users of natural resources. These motivations will likely remain in place as long as success continues to be measured in terms of area (km2) protected. 3. The global pattern of MPAs was reviewed and appears to be residual, supported by a rapid growth of large, remote MPAs. The extent to which MPAs in Australia are residual nationally and also regionally within the Great Barrier Reef (GBR) Marine Park was also examined. 4. Nationally, the recently announced Australian Commonwealth marine reserves were found to be strongly residual, making almost no difference to 'business as usual' for most ocean uses. Underlying this result was the imperative to minimize costs, but without the spatial constraints of explicit quantitative objectives for representing bioregions or the range of ecological features in highly protected zones. 5. In contrast, the 2004 rezoning of the GBR was exemplary, and the potential for residual protection was limited by applying a systematic set of planning principles, such as representing a minimum percentage of finely subdivided bioregions. Nonetheless, even at this scale, protection was uneven between bioregions. Within-bioregion heterogeneity might have led to no-take zones being established in areas unsuitable for trawling with a risk that species assemblages differ between areas protected and areas left available for trawling. 6. A simple four-step framework of questions for planners and policy makers is proposed to help reverse the emerging residual tendency of MPAs and maximize ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...

IDW 2022 was hosted in Seoul, the Republic of Korea, by the Korea Institute of Science and Technology Information (KISTI), committed by the Ministry of Science and ICT, Seoul Metropolitan Government, National Library of Korea, and National Assembly Library, with the support of the Korea Research Institute of Standards and Science (KRISS), Sungkyunkwan University (SKKU), Korea Institute of Oriental Medicine and the Korea Institute of Geoscience and Mineral Resources.This landmark event brought together data scientists, researchers, industry leaders, entrepreneurs, policymakers, and data stewards from disciplines across the globe to explore how best to exploit the data revolution to improve science and society through data-driven discovery and innovation. IDW 2022 combined the 19th RDA Plenary Meeting, the biannual meeting of this international member organization working to develop and support global infrastructure facilitating data sharing and reuse, and SciDataCon 2022, the scientific conference addressing the frontiers of data in research organized by CODATA and WDS. ; International audience ; One of the challenges in Machine Learning research is to ensure that the presented and published results are sound and reliable. Reproducibility is an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. We already went through the path of darkness: We proposed a set of recommendations ('fixes') to overcome these reproducibility challenges that a researcher may encounter in order to improve Reproducibility and Replicability (R&R) and reduce the likelihood of wasted effort. These strategies can be used as "swiss army knife" to move from DL to more general areas as they are organized as (i) the quality of the dataset (and associated metadata), (ii) the Deep Learning method, (iii) the implementation, and the infrastructure used. We identified the main challenges and constraints from these papers and presented ...