dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1998; Parker et al. , 2004; Woodruff et al. , 2005; Worley et al. , 2005; Woodruff et al. , 2011) , a global ocean marine meteorological and surface ocean dataset that is considered the most complete of its kind. It is comprised of measurements and observations extracted from many international data sources, including ship reports, moored and drifting buoys, coastal stations, and other marine platforms. ICOADS release 2. 4 was used in the ISPDv2 for the period 1952-2008 and ICOADS release 2. 5  (Woodruff et al. , 2011)  was used for the period  1785-1951 and 2009-2012. The tropical cyclone component was taken from the International Best Track Archive for Climate Stewardship (IBTrACS;  Knapp et al. , 2010). The IBTrACS dataset consists of global tropical cyclone best track position and intensity observations, and reports collected from each of the World Meteorological Organization (WMO) Regional Specialized Meteorological Centers, Tropical Cyclone Warning Centers, and other national agencies.
The IBTrACS Beta version was used for the years  1952-2006, version v01r01 for 1871-1883 and 1886-1951, version v02r01 for 1884-1885 and 2007-2008, version v03r02 for 2009-2010, and version v03r05 for 2011-2012. The inclusion of various versions of both ICOADS and IBTrACS in ISPDv2 was the result of using the most up-to-date data that were available during the assimilation into the Twentieth Century Reanalysis (20CR;  Compo et al. , 2011). This rationale is elaborated upon in Section 2. 1. The international surface pressure databank.
Dataset content and coverage The ISPDv2 dataset is stored in the Hierarchical Data Format version 5 (HDF5: http://www. hdfgroup. org), which allows for efficient archiving of large, complex data structures and a diverse set of metadata. Each HDF5 file contains 13 tables and 15 directory subgroups that define how to decode the individual records, and each observation record includes metadata defining the data collection source and observation type (e. g. station observation, marine observation, surface pressure observation from a radiosonde sounding). Because the ISPDv2 was assimilated as observational input into the 20CR, each record also includes the 20CR quality control and assimilation feedback metadata. These quality control and feedback metadata include the results of the five-step quality control procedure described in Appendix B of  Compo et al.
(2011)  and all the relevant statistical quantities returned by the 20CR ensemble data assimilation system. The format of the HDF5 metadata, therefore, is designed to allow traceability of observations from their original source to the ISPDv2, and to permit direct feedback from the 20CR data assimilation back to the original source archives. Each observation at any single observation time is assigned a unique number that when combined with the time stamp (year, month, date, hour, and minute) forms a unique identifier within the complete dataset. Thus, every observation has an unambiguous reference within the full ISPDv2 dataset, which allows for precise identification and traceability. This data management feature is critical for accurately adding additional metadata from other future reanalyses, and evolving the ISPDv2 to new versions while maintaining the usage provenance of each record.
The 20CR required hourly files from the ISPDv2 data for the data assimilation system, thus each data file in the ISPDv2 collection contains one hour of global observations. Based on this file organization and data coverage, a maximum of 24 data files per day and 8760 data files per (non-leap) year exist in the ISPDv2 collection. The total dataset volume is 491. 32 GB. The HDF5 data are available for download as either individual hourly data files, or as yearly tar files, which are tarballs of one entire year of HDF5 data files. The ISPDv2 is also available in NetCDF and ASCII column data formats, both of which are derived from the native HDF5 format. The data content within these two formats is not a complete reproduction of the tabular table content and metadata that are contained within the HDF5 data.
It does contain, however, essential information, including the 20CR quality control feedback information, for most users to carry out their research projects. The ASCII data are available as monthly tar files for direct download from the National Center for Atmospheric Research (NCAR) Research Data Archive (RDA) website (http://dx. doi. org/ 10. 5065/D6SQ8XDW), while the NetCDF data are produced on demand through the data subsetting service described in Section 3. 1. The data availability varies considerably over the period of record. Maps of annual land station distribution (Figure  1 ) highlight the steady growth in the number of reporting stations, and hence data coverage, since the year 1850 (station coverage maps for every year are available at http://www. esrl. noaa. gov/ psd/data/ISPD/v2.
0/). The sources of land station The total number of observations per year in the ISPDv2 increases steadily from approximately 100 000 records in 1870 to 53 million in 2010 (Figure  4 ). A local increase and subsequent decrease in this trend during the 1878-1894 period is attributed to the addition of 1. 8 million records from the US Marine Meteorological Journals and digitized for ICOADS Release 2. 5  (Woodruff et al. , 2011). A decrease during the World War I years is evident, while an increase during the World War II years is attributed to the inclusion of 1. 6 million ICOADS records digitized from the UK Royal Navy Ships' logbooks  (Brohan et al. , 2009; Woodruff et al. , 2011). A decrease during the mid-1960s is associated with the aforementioned decline in station records in the Integrated Surface Database  (Smith et al.
, 2011). A decrease in 2007 is most likely due to a delay in processing, and therefore incomplete records, in the Integrated Surface Database. Other small-scale variations in the curve likely can be correlated with increased scientific interest (local peaks) and research budget reductions (local troughs;  Fleming, 2000). The dataset contains reports from more than 10 000 land stations, which, in addition to the marine observing systems reports, comprise over 1. 5 billion observations in total. Between 1860 and 1917, the total number of marine and surface station observations is comparable, but in later years the total count.
Reanalysis The primary motivation for the creation and development of the ISPD was to facilitate progress on research and understanding of long-term trends and variations in global surface pressure. In particular, a long-term historical dataset, such as the ISPD, becomes essential to providing an observational underpinning to retrospective climate analysis datasets, commonly known as reanalyses. Reanalysis products are used extensively in climate research, applications, and services, including for monitoring and comparing current climate condi- The columns represent the station count for the years listed and organized by WMO regional block number (0 and 1 = Europe; 2 and 3 = Russian Federation and other former Soviet Union countries; 4 = Asia; 5 = China; 6 = Africa; 7 = North America, Central America, and the Caribbean; 8 = South America and Antarctica; and 9 = South-West Pacific).
The limited time range of these reanalyses restricts their usefulness for many climate research applications. The ISPD adds value by providing a much longer period of record and thus enables the development of reanalyses with longer time spans. The first reanalysis to make use of the ISPD is the 20CR Project  (Compo et al. , 2011) , which assimilates only the ISPDv2 surface and sea-level pressure observations and prescribes observed monthly seasurface temperature and sea-ice distributions from HadISST1. 1  (Rayner et al. , 2003)  as boundary conditions. This global reanalysis dataset spans the late 19th century, the entire 20th century, and the early 21st century. It estimates the state of the atmospherethe 'analysis'by combining the hourly and synoptic ISPDv2 observations in a 6-h time window with a dynamically generated 9-h first guess forecast initialized from the previous analysis.
Cycling this procedure with overlapping 6-h analyses and 9-h forecasts has the effect of spreading the observational information from the ISPD three-dimensionally in space and in time. As the 20CR was produced, the early period ISPDv2 'beta' version was revised to include newly digitized additions and improve the overall coverage of stations. As ICOADS Release 2. 5 was then available, it was also included to increase the number of marine observations in the early period. Similarly for IBTrACS, newly digitized records from the most recent versions of IBTrACS were added to ISPDv2 as it evolved and improved over time. The final ISPDv2, therefore, contains multiple source versions of ICOADS and IBTrACS. The newer versions of ICOADS and IBTrACS did not change appreciably except to expand their data inputs and coverage, therefore we do not anticipate that the use of multiple versions introduces inconsistencies in the pressure dataset.
These quality control and data assimilation results, which include the first-guess fields, analysis departures, bias estimates, and observation errors, were then written back into the ISPDv2 so that each observational record contains this feedback information. Future users of the ISPDv2, and those who contributed the original observational data, therefore can utilize this information to make an informed decision on the quality and usefulness of each observational record during the 20CR time period. One project that utilized the 20CR feedback metadata is the ERA-20C reanalysis  (Poli et al. , 2013) , which is the first reanalysis produced under the European Reanalysis of Global Climate Observations database project (ERA-CLIM: www. era-clim. eu). The ERA-20C is a global reanalysis that spans the 20th century  assimilating only surface and sealevel pressure observations from ISPD and ICOADS and marine surface winds from ICOADS.
Research data archive data location and accessibility The primary repository for ISPDv2 is in the RDA (http://rda. ucar. edu) at NCAR in Boulder, Colorado. The RDA is a free and open data collection where data discovery can be achieved through faceted searches based on Global Change Master Directory (GCMD: http://gcmd. nasa. gov) metadata keywords, free text queries, and lists highlighting the most used datasets. Data access is free, but requires each user to register through a simple online process that validates the submitted email address. The ISPDv2 data access is organized by year and month on the RDA website (http://dx. doi. org/ 10. 5065/D6SQ8XDW), and users may browse through the pages to locate specific data of interest. Data files are organized in default groups by observation year and month.
They can be downloaded directly from the RDA web interface using server-supplied 'wget' scripts. Users may select a collection of files to download from default lists by using the 'Web File Listing' option under the 'Data Access' tab. A more refined option is provided through the data subset request form, which may be accessed via the 'Get a subset' link under the 'Data Access' tab. This produces a customized data subset of the HDF5 data based on user-provided constraints. Users can specify the temporal limits, spatial domain, observation type (s), and ASCII or NetCDF data output formats through the data subset request form (Figure  5 ). The output file compression can also be requested on this form (not shown). Observations from individual observing stations may also be requested, which is a useful feature for users who wish to procure time series for specific locations or regions.
Dataset citation The RDA also provides a data citation service. The dataset homepage provides citation syntax in the standard forms recommended by the Federation of Earth System Information Partners (ESIP), American Meteorological Society (AMS), American Geophysical Union (AGU), DataCite, and the Geoscience Data Journal. This dataset citation is also available in Research Information System (RIS) format so that users may import the citation for this dataset directly into their citation reference management software (e. g. End-Note, Zotero, etc. ). One key element in the dataset citation is the data access date ('Accessed dd mmm yyyy'). Leveraging the fact that RDA data users are registered, a customized data citation can be prepared for the user by using the 'Get a customized data citation' link on the dataset home page.
File content metadata File content metadata are collected as part of the data processing and preparation of the ISPDv2 for the RDA. The metadata supports additional information services for interested users. The overall ISPDv2 metadata summary is provided from the dataset home page under the 'More Details' link. Here, tabulations of the observing station location, observation type, platform identification, and maps of the global distribution for the full ISPDv2 can be viewed (Figure  6 ). In addition, users may view the content metadata for the yearly tar file archives by clicking on a looking glass icon adjacent to the tar file listing, accessible under the 'Data Access' tab. This service enables users to query the ISPDv2 via an interactive map of land station locations, and more efficiently determine the observational data available at any particular time and region of interest (Figure  7 ).
ECMWF data location and accessibility A subset of the ISPDv2 is also archived at ECMWF (http://apps. ecmwf. int/datasets). Here, users may extract temporal subsets from the ISPDv2 archive stored at ECMWF and refine their selection by observation platform. The output from these requests is written in ASCII column format and contains the date, location, observed value, report type, unique identifier which allows traceability to other ISPD versions, and several feedback parameters from the 20CR, including the 20CR bias estimate, the 20CR ensemble mean first guess pressure minus the 'modified' observed pressure, and the ensemble mean analysis pressure minus the 'modified' observed pressure. The 'modified' pressure is the pressure value after the 20CR system adjusted it to be consistent with the orography in the assimilating model.
Future plans Since the release of ISPDv2, over 22 additional organizations have contributed to the station and marine components of the ISPD (Table  1 ) and the period of record now extends back to 1755. These new contributions have been incorporated into a newer version 3 of the ISPD (ISPDv3), which will be made available in the near future through the NCAR RDA. The observation feedback archive (OFA) version of this dataset as used in the ERA-20C reanalysis is available from ECMWF  (Hersbach et al. , 2015). The gain in observations for ISPDv3 is illustrated by the time series shown in Figure  8. Most of the increase occurs in the Northern Hemisphere, and there are visible increases in the late 19th century and during the World War I years. The 2007 decrease in merged records from the Integrated Surface Database into ISPDv2 (cf.
The ERA-20C global reanalysis, the first reanalysis under this project, spans the 20th century (1900-2010) assimilating only surface and sea-level pressure observations from ISPDv3 and marine wind observations from ICOADS 2. 5 using a 4D-Var assimilation system  (Poli et al. , 2013). In addition, ISPD version 3. 2. 9 is being assimilated into the next generation of the 20CR (version 2c). (Compo et al. , 2011; Br € onnimann et al. , 2012). This activity is currently underway and will be reported on in due course. The ISPD also will be used in the planned centennial coupled reanalysis being developed under the Japa- Comments and questions on the ISPD can be directed to the RDA data specialist or left at the reanalyses. org webpage for this dataset (http://reanalyses. org/ observations/international-surface-pressure-databank).
Acknowledgements Construction of the ISPD is supported by the  U. S. Department of Energy Office of Science  ( Biological and Environmental Research program ) and the  NOAA Climate Program Office. The Twentieth Century Reanalysis Project and the  International Surface Pressure Databank  used resources of the  National Energy Research Scientific Computing Center  and of the  Oak Ridge Leadership Computing Facility  at  Oak Ridge National Laboratory , which are supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231  and Contract No. DE-AC05-00OR22725 , respectively. Manola Brunet  and  Philip D. Jones  are grateful to the  European Union -funded projects  EUropean Reanalysis and Observations for Monitoring  ( EURO4M ,  FP7-SPACE-2009-1 Project No.
242093 ) and  Uncertainties in Ensembles of Regional Reanalyses  ( UERRA ,  FP7-SPACE-2013-1  Project No. 607193 ). Pavel Groisman was partially supported by Grant  14. B25. 31. 0026  of the  Russian Ministry of Education and Science. Renate Auchmann  was supported by the  Swiss National Science Foundation  (SNF; Project  TWIST ,  200021_146599/1 ). The authors thank the following individuals and organizations for their contributions: (1) Rajmund Przybylak of the  Nicolaus Copernicus University, Department of Meteorology and Climatology, Poland. (2)  Frank Le Blancq  and many others,  Jersey Met Service, for Channel Island data. (3) The IT group at  CIRES  and  NOAA/ESRL Physical Sciences Division. (4) Prashant Sardeshmukh of  University of Colorado/ CIRES  for valuable guidance and input.
Zentralanstalt f€ ur Meteorologie und Geodynamik 27. McGill University, Canada 28. Met Office Hadley Centre, UK a (ZAMG; Austrian Weather Service) a 29. M et eo-France a a Contributors to version 2 (cf. Compo et al. , 2011, Table II). 30. M et eo-France -Division of Climate 31. Meteorological and Hydrological Service, Croatia a 32. National Center for Atmospheric Research (NCAR), USA a 33. National Institute for Water and Atmospheric Research (NIWA), New Zealand 34. Nicolaus Copernicus University -Department of Meteorology and Climatology, Poland 35. Niue Meteorological Service 36. NOAA Climate Database Modernization Program (CDMP), USA a (continued).
, 0. 4) and (20, 25,. , 80), respectively, for the purpose of F fill calculation (x2. 2). A combined Fourier map is then obtained in the following way (step 3a in Fig. 1 ). Firstly, N calculated sets of Fourier map coefficients are split into n groups each containing N/n sets of map coefficients. The map coefficients in each group are then averaged, 5% of the map coefficients (reflections) are randomly removed from each averaged set of coefficients and a Fourier map is calculated with the remaining reflections. This is repeated for each group of map coefficients, resulting in n Fourier maps (each with 5% of the reflections randomly removed) that are then averaged in real space to yield a single The correct residue Lys107 (chain L) of PDB entry 1f8t (the rotamer on the left) is shown as a blue map calculated using (1) with w = 1.
Values of B sharp are tested in a defined range and the value that maximizes the map kurtosis is selected. Others have also pointed out that kurtosis may be used to characterize crystallographic maps (see, for example,  Lamzin & Wilson, 1993; Pai et al. , 2006). Below, we provide the basis for identifying map kurtosis as a metric for selecting B sharp. We examined how data and model properties, e. g. resolution and B factors, are related to map kurtosis and why it may be a good measure to use in selecting B sharp. For this, we used a test example of two atoms, Mg and O, placed inside a 10 Â 5 Â 5 A ˚box in space group P1 at approximately 3 A ˚distance from each other. Each atom has unit occupancy. Fig. 8 (a) shows the distribution of 1. 5 A resolution Fourier syntheses values along the Mg-O vector corresponding to three cases with all atoms having B factors equal to 10, 30 and 50 A ˚2, respectively.
PDB entry 2r24: the structure of aldose reductase obtained using neutron data We have examined the outcomes of FEM calculations for about a dozen neutron structures selected from the PDB. The general observations stated for X-ray structures also hold for structures refined against neutron data. Fig. 20  illustrates the application of FEM to a neutron structure. In this example the somewhat broken ligand density map from (1) improves after calculating the FEM.
PDB entry 2g38: a highly anisotropic data set This is a classical example of data with severe anisotropy and has served as an example for the Anisotropy Correction Server  (Strong et al. , 2006). The original diffraction data were  kindly provided by the author (Michael Sawaya), as the corresponding PDB entry contains only an anisotropycorrected version of the measured intensities. Fig. 21  shows three maps: the original map from (1), the FEM and a densitymodified RESOLVE map. The latter two maps show a notable reduction in anisotropy effects (elongation of map contours along the vertical direction). 3. 5. PDB entry 1se6: an incorrectly placed ligand  Pozharski et al. (2013)  pointed out a misfitted ligand in this entry. While the Fourier map from (1) does not clearly identify the ligand at 1 (Fig.
PDB entry 3i9q: FEM as a better target for real-space refinement To further illustrate the FEM procedure, we performed the following test. We selected PDB entry 3i9q (data resolution 1. 45 A ˚), which we re-refined using phenix. refine (Afonine et al. , 2012). Using this model, we calculated two maps: the FEM and the usual map from (1) (Figs. 24a  and 24b ). Since we wanted to compare the original A map  (Read, 1986)  with the FEM, no modeling of missing reflections, anisotropy correction or sharpening was performed. What is remarkable about this structure is that the map from (1) is outstandingly poor, perhaps owing to poor completeness in the low-resolution zone (50% completeness in the 17. 49-6. 58 A ˚zone). We then removed everything but the macromolecule from the model and subjected this model to 1000 independent moleculardynamics (MD) simulation runs using phenix.
Acknowledgements This work was supported by the  NIH  (Project  1P01 GM063210 ) and the  Phenix Industrial Consortium. This work was supported in part by the  US Department of Energy  under Contract No. DE-AC02-05CH11231. AU thanks the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  and Instruct, which is part of the  European Strategy Forum on Research Infrastructures (ESFRI). DT acknowledges support from  Structural Biology Grant   P0048  from the  Slovenian Research Agency.
Materials and Methods The experiment used (program and stimuli) is freely available from http://dx. doi. org/10. 6084/m9. figshare. 1287284. It runs under Matlab with the psychophysical toolbox  (Brainard, 1997; Kleiner et al. , 2007). The behavioral data and scripts used to analyze the data are available from http://dx. doi. org/10. 6084/m9. figshare. 1287262. The imaging radiological analysis is also available with the behavioral data. CT or MRI scans could not be shared as they belong to the UK National Health Service (NHS) and not to the research team. The study was approved by the NHS Lothian South East Scotland Research Ethics Committee 01 (REC reference number: 11/SS/0055) and NHS Lothian Research and Development (R&D project number: 2011/W/NEU/09). Participants: Twenty-five stroke patients (14 males, 11 females) with a median age of 69 years (min 39, max 85) were recruited into this study.