dataesr/scientific-paragraphs-categorization huggingface dataset:
force11. org) and the Research Data Alliance (RDA; https://www. rd-alliance. org). First and foremost, the working group is finalizing a set of recommendation to guide consumers and producers of standards and repositories to select and describe them, or recommend them in data policies, but it is also collaborating with other RDA and Force11 groups to define a common framework for research data policy, and identify criteria and develop tools for the selection of standards, databases and repositories, e. g. , when creating data management plans.
We say we need standards, but do we use them? The scientific community, including funders and publishers, endorses the concept that common data and metadata standards underpin data reproducibility, ensuring that the relevant elements of a dataset are reported and shared consistently and meaningfully. But navigating through the many standards available can be discouraging and often unappealing for prospective users. Bound by a particular discipline or domain, reporting standards are fragmented, with gaps and duplications. Understanding how they work or how to comply with them takes time and effort. FAIRsharing plays its part in providing a snapshot of the standards landscape. Figure  1  and Table  1  provide a manually curated view on the status quo. However, be aware that this landscape is dynamic and will continue to evolve as we engage with more communities to verify the information we house, add new standards, track their life-cycle status and usage, and link out to examples and training material, where available.
databases and repositories. However, there are additional 180 standards that should be explicitly mentioned in these data policies because they are implemented by the recommended databases and repositories. Analyzing the total 70 journals and publishers data policies curated in FAIRsharing, as of December 2017 (https://fairsharing. org/policies/?q=&selected_facets=subtype_exact:Journal), we find that 56 mention one or more specific standards; see Figure  2. When standards are recommended by data policies, the minimal reporting guidelines are recommended 1. 6 times more than terminology artifacts and models/formats, even if the latter two are heavily implemented by databases and repositories. In general, databases and repositories are 37 times more recommended than models/formats; even when a recommended database or repository implements specific models/formats, the latter are not explicitly mentioned by the data policies.
It would not be FAIR if standards were not executable The under-representation of recommended terminology artifacts and models/formats is of particular concern. Minimal reporting guidelines are intended for human consumption and are usually narrative in form and therefore prone to ambiguities, making compliance and validation difficult and approximated. Many of these guidelines however, already come with (or lead to the development of) associated models/formats and terminology artifacts, which instead are created for machine consumption. The latter are essential to the FAIR principles, which put a specific emphasis on enhancing the ability of machines to automatically discover and use data. In particular, the computability of metadata standards is core to the development of metrics of FAIRness to measure the level of compliance of a given dataset against the relevant metadata descriptors.
Help us to help you To promote the use of standards, databases and repositories and paint an accurate picture of their relationships, four main stakeholders can play catalytic roles. • Funding agencies. FAIRsharing helps you select resources to recommend in your data policy, or that awardees should consider when writing their data management plan. If we are to make FAIR data a reality, you should recognize standards as digital objects in their own right, with their associated research, development and educational activities  (5). New funding frameworks need to be created to provide catalytic support for this techno-social activities, in specific domains, within and across disciplines to enhance interoperability of data. No more hollow promises, it is time for hard outcomes. It won't be FAIR if was easy.
FAIRsharing helps you maintain an interrelated list of standards, databases and repositories, grouping those you want to recommend to your users (e. g. , see examples of recommendations created by eight main publishers and journals: https://fairsharing. org/recommendations; and the record of the UniProt Knowledgebase as an example of a highly recommended repository https://fairsharing. org/biodbcore-000544). You can learn more about standards, especially those implemented by databases and repositories you already recommend, as you should explicitly mention these standards in your policy. As we continue to map the landscape, you can also revise your selections over time, recommending further resources with more confidence. FAIRsharing provides you with a base to create or enrich training material on the role and use of standards in databases and repositories to enable research data management and reproducibility.
Acknowledgements We thank all our collaborators and contributors to the current resource and its precursors (BioSharing and the  Minimum Information about a Biomedical or Biological Investigation, MIBBI portal ), in particular the chairs and the members of our international  Advisory Board , the  Force11  and  RDA WGs and our Adopters community. We specifically thank key (past and present) contributors to the resource, notably  Melanie Adekale ,  Delphine Dauga ,  Eamonn Maguire ,  Annapaola Santarsiero , and  Chris Taylor. The authors are funded by grants awarded to S. A. -S. that include elements of FAIRsharing; specifically grants from the  UK BBSRC and Research Councils  ( BB/L024101/1 ,  BB/L005069/1 ),  EU  ( H2020-EU. 3. 1, 634107 ,  H2020-EU. 1. 4. 1. 3 , 654241 ,  H2020-EU. 1. 4. 1.
org/bs guideline g-s000174 5. MlxS-MIGS/MIMS Reporting 170 Ready 0 2 https://fairsharing. org/bs guideline g-s000161 6. MIxS Reporting 158 Ready 3 3 https://fairsharing. org/bs guideline g-s000518 Activating the decision-making chain is an essential step. When a standard is mature and appropriate standard-compliant systems become available, such as databases and repositories, these must then be channelled to the relevant stakeholder community, who in turn must recommend them (e. g. in data policies) or use them (e. g. to define a data management plan) to facilitate a high-quality research cycle. To understand how journals and publishers select the resources to recommend, we have worked closely with the editors of eight journals/publishers, whose data policies are quite well developed. The resources that EMBO Press, F1000Research, Oxford University Press' GigaScience, PLOS, Elsevier, Wellcome Trust'.
editors, publishers or an organization with a data policy. FAIRsharing helps you maintain an interrelated list of standards, databases and repositories, grouping those you want to recommend to your users (e. g. , see examples of recommendations created by eight main publishers and journals: https://fairsharing. org/recommendations; and the record of the UniProt Knowledgebase as an example of a highly recommended repository https://fairsharing. org/biodbcore-000544). You can learn more about standards, especially those implemented by databases and repositories you already recommend, as you should explicitly mention these standards in your policy. As we continue to map the landscape, you can also revise your selections over time, recommending further resources with more confidence. FAIRsharing provides you with a base to create or enrich training material on the role and use of standards in databases and repositories to enable research data management and reproducibility.
Enhancing both the capability and skills of those involved in producing, managing, serving, curating, preserving, publishing or regulating data (and other digital objects) is a vital step to reduce the knowledge gap on standards that is currently found in the research community. collection for computational models in biology networks: https://fairsharing. org/collection/ComputationalModelingCOMBINE). • Developers and curators of standards, databases, repositories. FAIRsharing helps you to make your resources more discoverable, gain increased exposure and credit outside of your immediate community, promoting adoption (learn how to add your resource to FAIRsharing, or claim it, at https://fairsharing. org/new). o As the representative of a community standardization initiative, you are best placed to describe the status of your standards and track their evolution (creating an individual record e.
g. , the DDI standard for social, behavioral, economic, and health data: https://fairsharing. org/bsg-s000605; or grouping several records in a collection e. g. , the HUPO PSI standards for proteomics and interactomics data: https://fairsharing. org/collection/HUPOPSI). If you strive for FAIR data, then you need to ensure you also deliver linked data models that allow the publishing and connecting of structured data on the web. o Similarly, as representative of a database or repository, you are uniquely placed to describe your resource, and to declare the standards you implement (e. g. , the ICPSR archive of behavioral and social science research data that uses the DDI standard: https://fairsharing. org/biodbcore-000936; or the Reactome knowledge base, https://fairsharing. org/biodbcore-000329, which uses several standards in the COMBINE • Journal• Trainers and educators.
Only a handful of journalists are dedicated to climate and environmental issues in the Norwegian media, and climate journalism is a relatively new field in scientific journalism. This study is based on qualitative interview material consisting of semi-structured interviews with six journalists and five climate researchers. The analysis of the interview data focuses on the communication relationship between researchers and journalists, based on the IPCC reports in 2013-2014. We find that climate researchers are quite happy with the climate reporting, and that the journalists, for their part, want to communicate this field more broadly, but that the dissemination is threatened by cuts that may lead to lower prioritization by the news desks.
Site selection and data sources We followed the protocols of the Global Yield Gap Atlas (www. yieldgap. org) to determine Y w and Y g for legume crops in Ethiopia, Kenya, and Tanzania  (Grassini et al. , 2015b; van Bussel et al. , 2015). Briefly, we selected sites for each crop-country combination based on (i) a climate zone (CZ) scheme that accounts for variation in growing degree days, temperature seasonality, and aridity index  (Van Wart et al. , 2013) , (ii) distribution of crop area as reported by SPAM 2005 maps  (You et al. , 2014a (You et al. , , 2014b)) , and (iii) availability of meteorological stations with daily weather data. Within each country, CZs with > 5% of total national harvested area for each crop were selected. Within each CZ, a 100-km radius 'buffer' surrounding each weather station was created and clipped by the borders of the CZ to ensure that the buffer zone is located within a unique CZ.
Since 52% of the buffers had less than 10 years of weather data but at least 3 years, long weather data records were generated using the method described by  Van Wart et al. (2015). In short, this method corrects long-term daily gridded NASA-POWER maximum and minimum temperature based on correlations between measured and gridded weather and uses uncorrected NASA-POWER solar radiation and TRMM rainfall to generate long-term synthetic weather files. Finally, for buffer zones without any measured weather data (48% of total buffers), we used uncorrected gridded weather data from NASA-POWER. Within each buffer zone, up to three dominant soil types were selected, based on the distribution of the harvested area of the target crop within the buffer. Soil data were retrieved from both AfSIS-GYGA functional soil information of sub-Saharan Africa database (maximum effective depth of water extraction from soil by roots, maximum soil depth, volumetric soil water content available for extraction by crop roots)  (Leenaars et al.
, 2015 (Leenaars et al. , , 2018) )  and ISRIC-World soil information, WISE international soil profile dataset (drainage)  (Batjes, 2012)   (CSA-Ethiopia, 2004 , 2005 , 2006 , 2007 , 2008 , 2009 , 2010 , 2011 , 2012 , 2013 , 2014 , 2015 , 2016) ; for Kenya a 5-year average centered on year 2005 was obtained from SPAM 2005  (You et al. , 2014a (You et al. , , 2014b)) ; and for Tanzania four years  (2003, 2008, 2013, 2015)  of district level data from the National Bureau of Statistics Tanzania were used (NBS-Tanzania, 2012, 2016a,b).
Estimation of yield gaps for legume crops and data analysis For each buffer, Y w was simulated for each of three dominant soil types. Average Y w was calculated for each buffer using a weighted average based on the proportion of soils within the buffer. Y g was calculated as the difference between long-term average Y w and Y a. Relative yield gap (ReY g ; expressed as percentage of Y w ) was calculated as one minus the ratio between Y a and Y w times 100. Results were upscaled from buffer to CZ and from CZ to country level using the bottom-up approach described by  van Bussel et al. (2015). In short, the weighted average of the yields per soil type was taken to obtain the yield per crop cycle for a buffer. In case of multiple crop cycles per year the average was taken to obtain the yield per crop  (Van Bussel et al.
Data on benchmarking legume production in SSA This study was the first to assess, at a regional level, the yield gaps (Y g ) of legume crops in East Africa (E-Afr), and to investigate opportunities of different levels of yield gap closure to achieve future (2050) legume self-sufficiency on existing cropland. We followed the protocols of the Global Yield Gap Atlas to map Y g in an agronomically robust and reproducible manner, based on a bottom-up approach using local data as input and involving local experts to evaluate model results. For a robust analysis, one of the requirements is that all input data meets the minimum quality standards at the appropriate spatial scale  (Grassini et al. , 2015a). Given the known data limitations in SSA, our assessment helps in identifying priority data gaps that need attention.
Acknowledgments This work was implemented as part of the  CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) , which is carried out with support from  CGIAR Fund Donors  and through bilateral funding agreements. For details please visit https://ccafs. cgiar. org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organisations. We thank  Tom Sinclair  from  North Carolina State University, Raleigh USA , for his support in using the SSM, and  Ken Giller  from  Wageningen University  and leader of the  N2Africa  project and  Lieven Claessens  from  IITA  for establishing contacts with several local agronomists and for discussing the results with us. We thank local agronomists of  Ethiopia (Kindie Tesfaye ), Tanzania ( Abubakari Mzanda  and  Musa Tamba ) and of  Kenya  ( John Ojiem ,  Joyce Maling 'a and  Ganga Rao ) for providing data and evaluating model results.
MicroRNAs and Lung Cancer Several miRNAs contribute to the development and the progression of several cancers by controlling, for example, cell growth, tissue differentiation, and apoptosis. These miRNAs can function as tumour suppressors or oncogenes, and belong to the so called 'oncomiRs'  [68]. They are also known to repress major cancer-related genes and might be considered as useful tools for diagnosis and prognosis. Finally, some of them are even considered as new therapeutic targets leading to the development of a preclinical study (NCT01829971)  [69, 70]. In lung cancer, many studies are descriptive, by analysing miRNA expression profile in lung tumour, in comparison to normal adjacent tissue. For some of them, a link between deregulated miRNAs and pathways was established using in silico approaches  [71] [72] [73].
Numerical model A three-dimensional numerical model was developed by means of the general purpose nonlinear finite element analysis package ABAQUS  [18]. The model used a sequentially coupled thermal-stress analysis procedure, and was described in detail in a previous paper  [16]. Geometric and material nonlinearities were accounted for in the numerical model. The model made use of the main heat transfer parameters recommended by EN 1991-1-2  [19]. The thermal resistance at the steel-concrete interface was taken into account through a constant value of 200 W/m 2 K prescribed as a gap conductance. The initial geometric imperfection of the columns was considered by importing the first buckling mode shape of a hinged column, amplified by a factor of L/1000. The model was meshed with three-dimensional eight-noded solid elements for both the steel tube and the concrete core, and two-noded solid elements for the reinforcing bars.
In turn, the Concrete Damage Plasticity (CDP) model was used for modelling the mechanical behaviour of concrete, which allows for a detailed definition of tensile and compressive behaviour of the material. Details of the input parameters used for the CDP model are provided in  [16]. The constitutive models selected for representing the uniaxial behaviour of steel and concrete at elevated temperatures were those given by EN 1993-1-2  [20]  and EN 1992-1-2  [21] , respectively. The numerical model is able to reproduce in a realistic way the fire performance of CFST columns with different cross-section shapes (CHS, SHS, EHS, RHS) and loading conditions (concentric and eccentric load), being fully validated with an extensive experimental database from the previous Project FRISCC  [14, 16].
5. All the columns were numerically simulated with the previously described numerical model, and the failure time was obtained for the load level applied in each case. From the results of the 5046 numerical simulations, an extensive numerical database of Nfi,Edtfi relationships was obtained. It should be pointed out that, from the initial batch of analysis cases, some of them (less than 2% of the total number) had to be removed from the database due to excessive long fire resistance time beyond the scope of application of the simplified design method (tfi > would not be practical.
ACKNOWLEDGEMENTS The authors would like to express their sincere gratitude to the  "Conselleria d'Educació, Investigació, Cultura i Esport" of the Valencian Community (Spain ) for the help provided through project  GV/2017/026. The authors also acknowledge the funding received from the  European Committee for Standardization (CEN)  for the Project Team  SC4. T4  of  CEN/TC250 , through the grant agreement  CEN/2014-02 Mandate M/515 Phase 1.
The measured contact angle on that sample was found to lie within error of the value measured on an unstrained PC film. A remaining question concerns the origin of the sign change between the slopes of the data sets for the two test liquids (Figs. 3(a, b ) and 3(c,d) (circles)). The striking difference between the two liquids is that DIM (Figs. 3(a, b )) is non-polar whereas glycerol (Figs. 3(c, d )) is highly polar. To provide further evidence for the important role of polarity, we perform contact angle measurements with water on PSf. For the droplet sizes relevant to our experiments, the evaporation rate of water is too high to perform robust measurements of θ Y. Instead, we perform advancing and receding contact angle measurements. The advancing and receding contact angles of water on PSf decreased by 7 ± 4 • and 11 ± 3 • over a 7 % strain, implying an increase in γ sv -γ sl.
ACKNOWLEDGMENTS The financial support by  Natural Science and Engineering Research Council of Canada  is gratefully acknowledged. The authors thank the  Global Station  for  Soft Matter , a project of  Global Institution for Collaborative Research and Education at Hokkaido University , as well as the Joliot chair from  ESPCI Paris. We thank  Y. Amarouchene ,  B. Andreotti ,  A. Antkowiak ,  L. Bureau ,  A. Chateauminois ,  M. Chaudhury ,  K. Daniels ,  C. Drummond ,  E. Dufresne ,  A. Hourlier-Fargette ,  H. Perrin ,  P. Rambach ,  F. Restagno ,  J. Snoeijer ,  R. Style , and  Q. Xu  for valuable suggestions and comments.
(2000)  -though it does include some areas below 300 masl. The underlying database forms the foundation for a global inventory of mountain areas and their biodiversity (http://www. mountainbiodiversity. org) which is being used as the basis for research (e. g. ,  Nürk et al. 2018). In addition to these two maps which focus only on mountains, two recent global maps identify mountains as specific landforms  (Sayre et al. 2014; Karagulle et al. 2017). The former map, commissioned by the inter-governmental Group on Earth Observations (GEO), is at a spatial resolution of 250 m: pixels with a slope of >8% and relative relief of 91-400 m are identified as low mountains, and those with a relative relief >400 m as high mountains. Landforms are only one of the four layers of this map; the others represent bioclimate, lithology, and land cover.
An elevation approach to mountains As noted above, in 2009 the Ministry of Regional and Municipality Affairs commissioned a project to adapt the approach used by Nordregio (2004) to fit the South Norwegian context: i. e. Norway from the county of Nord-Trøndelag and southward. Why the commissioned project was limited to mountains in Southern Norway, and did not address the entire country, is unclear. In later analysis, the topographical analysis was extended to include the rest of the country, as discussed below. The project had three objectives, with a clear policy intent: (1) to identify, delimit and characterize mountain areas and mountain municipalities; (2) to analyse the characteristics of mountain municipalities, compatible with existing national datasets; (3) to shed light on current challenges, difficulties and opportunities for mountain municipalities.
Discussion and Conclusions The primary focus of this paper has been on Europe and its countries, as this is where the majority of experiences of mapping mountain areas have emerged. However, atlases of the mountain areas of other continents (Africa: UNEP 2014; Hindu Kush-Himalaya: Zurick and Pacheco 2006) have also been produced, as have atlases and databases of transnational mountain ranges in Europe (e. g. ,  Alps: Tappeiner et al. 2008; Carpathians: Ruffini and Ptacek 2009; Pyrenees: SIG-Pyrenees 2018). With the exception of the first of these, which defined mountains as land above 1500 masl, the areas mapped have been defined using administrative criteria, respectively: the area considered by the International Centre for Integrated Mountain Development (ICIMOD 2018); and the perimeters of the territory concerning the Alpine and Carpathian Conventions and the Working Community for the Pyrenees.
Data Analysis and Statistics Raw data were preprocessed using custom-developed routines in MATLAB environment (MathWorks, United States). The wide-band signal was down-sampled to 1000 Hz and used as the LFP signal. Positive polarity is shown as up in all figures. For action potential detection, the raw wide-band signal was filtered (bandpass 300-5000 Hz) and negative events exceeding five standard deviations calculated over the most silent 1 s length segment of the filtered trace were considered as spikes (MUA). SEPs were detected as the first LFP troughs of sensory evoked responses. LFPs and extracellular spikes were analyzed by custom-written, MATLAB-based programs. Current-source density (CSD) analysis across cortical depth was used to eliminate volume conduction and localize synaptic currents.