dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the treaties were identified and collected by Ronald  Mitchell (2003)   [26]. We conducted a content analysis using the qualitative data analysis software NVivo to identify provisions linked to human health in these environmental treaties. We first used an extensive list of keywords relating to human health to identify a wide range of provisions. We reduced the initial sample, by applying a narrow definition of human health. For example, provisions on animal health, welfare, food supply and sanitary measures were excluded unless they explicitly referred to human health. Then, we classified genuinely health-related provisions into 14 different categories. We developed a detailed codebook with inclusion and exclusion rules for each of the 14 categories. We instructed a team of trained research assistants to read all 2280 treaties, using the codebook to identify any provisions that fitted our criteria.
Introduction Crystallography is the method of choice for obtaining atomic scale structural information on biological macromolecules and has contributed significantly to the development of structural biology, as shown by the number of structures present in the Protein Data Bank (PDB; https://www. rcsb. org/). Crystallography has thus contributed to the understanding of protein functions, explaining complex life mechanisms and aiding in development of new drugs based on rational design. For half a century, the method has undergone several revolutions, enabling it to maintain, during the past ten years, a PDB deposition rate of 6000-10 000 structures per year. In a non-exhaustive way, we can quote (i) automation of the crystallogenesis process by means of pipetting robots, allowing the use of ever smaller volumes of biological material (nanodroplet crystallization)  (Santarsiero et al.
, 2017). Data statistics are summarized in Table  1. De novo phasing was done in the CRANK2 pipeline (Skubak & Pannu, 2013) without any attempt to optimize either the structure determination process or the automatic model building. Samples purified from M. thermolithotrophicus: for phasing purposes with the crystallophore, crystals were used directly, i. e. as cocrystallized with 10 mM Tb-Xo4, or soaked for a short period in a solution containing 50 or 100 mM Tb-Xo4 (Table  S1 ). Crystals were flash-frozen in liquid nitrogen directly or stepwise by soaking in a cryo-preserving solution, as summarized in Table  S1. Conventional macromolecular crystallography experiments were performed: diffraction data were collected on a single crystal on different beamlines at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) as well as on Proxima-2A at synchrotron SOLEIL (Saint Aubin, France) as detailed in Tables 2-4 below.
Diffraction frames were integrated using the program XDS  (Kabsch, 2010) , and the integrated intensities were scaled and merged with the programs SCALA and TRUNCATE from the CCP4 program suite  (Collaborative Computational Project, Number 4, 1994). Data statistics are summarized in Tables  2 3 4. Single-wavelength anomalous dispersion (SAD) phasing was performed by AUTOSHARP  (Vonrhein et al. , 2007)  with substructure determination via SHELXD  (Sheldrick, 2008) , with standard defaults. Molecular replacement was done with PHASER  (McCoy et al. , 2007). In all cases, the Buccaneer software  (Cowtan, 2006)  was used for automated model building. Atomic models were manually improved in COOT  (Emsley et al. , 2010). Raw data used for de novo phasing have been made freely available on the Zenodo repository.
Refinement All of the models were optimized through iterative rounds of refinement and model building. Refinements were done using BUSTER (version 2. 10. 2, Global Phasing Ltd, UK). At all stages of refinement, non-crystallographic symmetry, translation-libration-screw motion and automatic water finding, as provided in BUSTER, were applied. After the first round of refinement, an anomalous Fourier synthesis was systematically computed to accurately place the terbium atoms. Figures were generated with PyMOL (version 1. 7. 2, Schro ¨dinger, LLC). Crystallographic software support was provided by SBGrid  (Morin et al. , 2013). Refinement statistics are reported in Table  S2. Structures and associated structure factor amplitudes have been deposited in the Protein Data Bank.
A successful MeshAndCollect phasing with multiple loops. P. horikoshii protease 1 (PhP1) crystallization was performed in sodium malonate pH 5. 5 (Table  S1 ). Crystals with dimensions ranging from 10 Â 10 Â 10 mm to 30 Â 30 Â 30 mm were used. Data collection was performed with a beam size of 50 Â 30 mm. Data were collected on four different mesh loops corresponding to four different crystallization drops, leading to 81 sub-data sets. Among them 27 were selected and merged by HCA using ccCluster  (Santoni et al. , 2017)  with a distance definition based on the correlation between intensities and with a coefficient linkage threshold value of 0. 3 [Fig. 1(a) ], resulting in statistics indicated in Table  1. Despite the soaking in the concentrated solution of Tb-Xo4, half of the crystals, coming from four different supports, were sufficiently isomorphous to allow the merging of their respective data sets.
Merging of data resulted in a 2. 0 A ˚resolution data set (Table  1 ) with good mean I / ( I ) values (14. 3 and 3. 2 overall and in the highest-resolution shell, respectively) as well as excellent data indicators (CC 1/2,high = 88. 2% and CC 1/2,overall = 99. 7%; R pim,high = 30. 7% and R pim,overall = 3. 6%). The presence of good anomalous information, as illustrated by the clear peaks in the anomalous Patterson map [Fig. 1(b) ], facilitated substructure determination and phasing, leading to a complete automatically built model  [Fig. 1(d) ] thanks to an easily interpretable electron density map [Fig. 1(c) ]. The structure was then fully refined as the crystallization condition used was new (PDB code 6hf6, Table  S2 ).
60 A ˚as well as a significant proportion of twinning. The addition of Tb-Xo4 resulted in crystals with a new morphology [Fig. 3(c) ]. Recording of diffraction data on these crystals was carried out on beamline ID23-1 (ESRF, Grenoble). The addition of 10 mM Tb-Xo4 led to crystals diffracting up to 1. 60 A ˚resolution. In addition, these data no longer showed twinning. They, therefore, made it possible to easily carry out a successful molecular replacement using a homologous model (PDB code 4lnf;  Murray et al. , 2013). In this particular case, Tb-Xo4, by providing a new crystallization condition, has simultaneously solved the two issues that had hindered the GlnA structure determination. A full description of the GlnA structure and associated biology will be published elsewhere.
The two crystal forms belong to the same P2 1 space group with different cell parameters and different asymmetric units, consisting of two biological tetramers and one tetramer, respectively. The presence of 10 mM Tb-Xo4 was sufficient in both cases for successful SAD phasing (Table  2  and Fig. 4 ). Despite the fact that crystal form 1 diffracted to a lower resolution and possessed a larger asymmetric unit (ASU) content (368 kDa), it was possible to extract an interpretable experimental electron density map [Fig. 4(a) ] by collecting the data at the Tb L III -absorption edge in order to maximize the terbium anomalous contribution. In that case, 75% of the atomic model could be built automatically with the Buccaneer software  (Cowtan, 2006). Even when data are collected far from the edge (the terbium anomalous contribution f 00 is about 7.
Crystal form 1 was obtained in two crystallization conditions (Table  S1 ). Contrary to FprA which was directly phased from the 10 mM Tb-Xo4 added during crystallization, the cocrystallization of ThiS in the presence of 10 mM Tb-Xo4 was not sufficient to derive useful phases for SAD phasing from crystal form 1. We thus applied a 9 min soaking of one native crystal into a cryo-preserving solution containing 100 mM Tb-Xo4 (Table  S1 ). Data collection at the Tb L III -absorption edge led to SAD phases of excellent quality, providing an interpretable experimental electron density map at 2. 55 A ˚resolution, as illustrated by the complete model (six protein chains, 1566 amino acids in the ASU) automatically built by the   Buccaneer software  [Fig. 5(a) ]. This example shows that Tb-Xo4 can be used as a conventional heavy atom through soaking of native crystals.
Crystalline form 2 was solved by molecular replacement with  Phaser (McCoy et al. , 2007)  using as search model the structure of ThiS form 1. Finally, the third crystalline form, co-crystallized in the presence of 10 mM Tb-Xo4, was directly frozen in liquid nitrogen and exhibited an exploitable anomalous signal. We managed to solve the structure without an extra soak of Tb-Xo4, and 80% of the atomic model could be built automatically with the Buccaneer software. The model of ThiS corresponding to form 1 soaked with 100 mM Tb-Xo4 was fully refined (Table  S2 , PDB code 6hk1). The model is composed of six protein chains, and 49 Tb sites were modelled with refined occupancies ranging from 0. 1 to 0. 58. By using this complete refined model, we computed an anomalous Fourier synthesis for crystal form 3 and observed the fixation of one Tb per monomer [Fig.
5(b) ]. 3. 2. 4. An example of ab initio phasing. As illustrated in Fig. 3 (a), fraction B contains several proteins. We successfully obtained different crystallization hits for this protein mixture in the presence of 10 mM Tb-Xo4. In the absence of the crystallophore, no crystal was obtained in the same conditions. Because of the presence of multiple proteins in this fraction, it was quite difficult to assign the protein identity to the crystal by using mass spectrometry. Consequently, to ensure the efficiency of the phasing as described in Section 3. 1, we systematically soaked all the crystals in a solution containing 50 mM Tb-Xo4, and we collected the data at the L IIIabsorption edge to maximize the terbium anomalous contribution. Among the recorded data sets, one presented diffraction at 2 A ˚resolution and a significant amount of anomalous signal (Table  4 ).
A tentative SAD phasing with AutoSharp  (Vonrhein et al. , 2007)  resulted in a perfectly interpretable electron density map, allowing a small portion of the model to be built manually (Fig. 6 ). The protein was sequenced using the electron density, and a search for the sequence in the genomic database of M. thermolithotrophicus matched with adenylate kinase (AdkA), a trimeric enzyme that catalyses the interconversion of adenine-type nucleotides Table  3  Data-processing statistics obtained from the different crystal forms of the ThiS protein. Values in parentheses refer to the highest-resolution shell. ThiS (10 mM Tb-Xo4) ThiS (soaked with 100 mM Tb-Xo4) Crystal form 1 Crystal form 1  (  Criswell et al. , 2003). By providing the full sequence, the automatic building completed the model at 95%.
The model was then fully refined (Table  S2 , PDB code 6hf7). This led us to identify four Tb-Xo4 binding sites per trimer with refined occupancies ranging from 0. 4 to 0. 7. Wavelength (A ˚) 1. 649 1. 649 Beamline ID23-1 (ESRF) ID23-1 (ESRF) Space group I422 I422 Unit cell (A ˚) a = b =.
Funding information This work was supported by grants from the  French ANR agency (program Ln23   ANR-13-BS07-0007-01  to SE, FR, CB, OM and EG) and from the  Max Planck Society  (to TW and SS). We are also grateful to the  SATT Pulsalys (Lyon)  for the maturation programme ( L-1382 ). The Grenoble Instruct Center was supported by the  French Infrastructure for Integrated Structural Biology Initiative FRISBI  ( ANR-10-INSB-05-02 ) and by the  Grenoble Alliance for Integrated Structural Cell Biology GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB).
Creation of physical skull models Six physical skull models were prepared, based on the same post-operative clinical CT scan acquired at a resolution of 0. 488 x 0. 488 x 1. 2 mm and at 120 kV and 400 mA. Two distinct skull geometries were created (with and without the excision) using Simpleware ScanIP v7 (https://www. simpleware. com/software/scanip/) (Figure  1 ). The data were imported as a 32-bit float, but were converted to an 8-bit float and resampled (using the cubic function) to match the voxel axial dimension to the in-plane dimension with the resulting cubic voxels of side length 0. 488 mm. A series of masks was then applied, with the appropriate grayscale thresholding and flood fill operations to select only bone matter within the scanned volume. A Gaussian smoothing filter was used to better represent the skull geometry and negate as much as possible to the stepped appearance of voxel-based output from CT scanners.
DIC data were acquired via a series of images captured by two V12. 1 Phantom highspeed cameras that were set at a stereo incident angle of 25°, recording at 15,000 fps. The camera was commanded using PCC standalone software (https://www. highspeedcameras. com/Service-Support/PCC-Software) which provided a falling edge signal input from the drop tower to ensure synchronous capture. The data were analysed using Aramis GOM software (http://www. gom. com/metrologysystems/system-overview/aramis. html) with quadrangle facets at 19 pixels (size) and 17 pixels (step), 484 3D points generated over 176 stages, and an average intersection deviation of 0. 0150 mm. Due to the confined space of the drop-tower base, artificial lighting was installed to provide consistent lighting conditions. This was achieved using three light sources, namely two conventional halogen lights with focal adjustment, and a light emitting diode (LED) Cree flood light acting as a background source, which was reflected onto the object using a flat mirror placed outside the impact area.
The drop-tower data also revealed consistent velocity decay in each of the skulls (Figure  9 ). The graduation tendency was similar in the T1 and T2 test environments, with only the magnitude (representing impact speed) differing between the test series. The force decay was also similar in the T1 and T2 test series (Figure  10 ) with the only major difference being the magnitude of the force, even though the T2 test series induced failure in the models. Figure  11  shows the test screen in Aramis GOM software which displayed synchronous behaviours of load, strain mapping, video play and range of strain all evident in one screen frame. The synchronicity of all these signals allows interpretations to be made relatively easily as to the occurrence of events, they sequence and how they are related to each other.
However, this also meant there was no need to take into account biological variations such as dry versus wet bone properties, because the material in all models was uniform ABS. Other parameters such as fall energy, constraints and contact surfaces were also replicated in the best possible manner. As a striking surface, material similar in mechanical properties to the silicone in the Humanetics Hybrid III headform was used in order to simulate the skin-muscle-hair interface. Because we carried out a comparative study, both the affected and non-affected skull models had the same material properties, these of ABS, thus allowing comparisons to be made primarily driven by the geometric effect of the bone excision. Furthermore, the biopsy hole was filled artificially in silico using the ScanIP software, which therefore only approximates the actual shape of a normal skull.
Satellite tracking is being coordinated through the MEOP consortium, which stands for Marine Mammals Exploring the Oceans Pole to Pole 4. MEOP brings together several national programs to produce a comprehensive quality-controlled database of oceanographic data obtained in polar regions from instrumented marine mammals. Over 500,000 vertical profiles of temperature and salinity have been collected since 2004 in the world ocean by attaching tags on marine mammals, such as Southern elephant seals. These data are complementary to those collected by Argo and it has been demonstrated that assimilating the temperature profiles into a global ocean forecast model has a positive impact in the predicted temperature and salinity in seal-sampled areas where other observational data are sparse  (Carse et al.
This should be strongly encouraged and supported. The JCOMM Open Access Global Telecommunication System (GTS) pilot project is an exciting development that has potential to greatly enhance oceanographic data assembly and exchange. On one hand, the rigor and robustness of the WMO GTS sets a standard for which the oceanographic community can aim. On the other hand, many in the oceanographic community currently find it difficult to get data into and out of the GTS, limiting its broader utility. The Open Access GTS pilot project aims to retrieve newly inserted data from the GTS, decode it from the WMO Binary Universal Form for the Representation of meteorological data (BUFR) format, add the data and metadata to a database, and provide access via web-accessible tools and visualizations. Expansion of GOOS to encompass biological EOVs and continental shelf and coastal marine systems presents some distinctive challenges in terms of data access, assembly, and exchange.
, 2009). Bias corrections are needed not only between satellite and in situ observations  (Reynolds et al. , 2002)  but also between in situ observations themselves  (Smith et al. , 2008; Huang et al. , 2017; Huang et al. (2018)  or between satellite observations themselves  (Yang et al. , 2016). In  Huang et al. (2017) , a systematic shipbuoy SST offset of about 0. 12 • C was found and corrected before merging the ship-buoy SSTs into a gridded dataset. Similarly, a systematic Argo float SST and buoy SST offset of about -0. 03 • C was found and corrected, and in  Huang et al. (2018) , the relative roles of Argo floats and moored/surface drifting buoys are analyzed. Various groups have established databases for quality monitoring of in situ and satellite data and blended products [e. g. , NOAA's in situ SST quality monitor (iQuam); Xu and Ignatov, 2014 and SST quality monitor (sQuam;  Dash et al.
For biogeochemical variables,  Amin et al. (2015)  assessed GOES satellite-based ocean color products using in situ networks  (Amin et al. , 2015). Land et al. (2018)  used a database of satellite in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables for ocean color chlorophyll-a and showed that most errors are correctable biases  (Land et al. , 2018). Martínez-Vicente et al. (  2017 ) examined the differences among phytoplankton carbon (Cphy) estimations from six satellite ocean color algorithms by comparison with in situ estimates, and large (>100%) biases have been found  (Martínez-Vicente et al. , 2017)   (Chatterjee et al. , 2017)  and creation of surface seawater pCO 2 and CO 2 flux maps from observation-based algorithms applied to satellite SST and color  (Feely et al.
OSCAR/Requirements Understanding the various user requirements for observational data is fundamental to the design and evolution of an integrated observing system, and the OSCAR/Requirements database provides the official repository of quantitative and technology free observations user requirements in support of the WMO and co-sponsored programs. WMO has defined its application areas, a number of which require marine/ocean observations: climate monitoring (including reanalysis), climate science, global NWP, high resolution NWP, nowcasting and very short range forecasting, seasonal to longer predictions and ocean applications (including marine services), each with its own user requirements. The database contains the observational user requirements for around 300 different geophysical variables expressed in terms of six criteria: horizontal resolution, vertical resolution, observing cycle (periodicity), timeliness, uncertainty and stability.
For each of these criteria, three values are determined: goal (the ideal capability above which further improvements are not necessary); threshold (the minimum requirement to be met to ensure that data are useful); and breakthrough (an intermediate level between threshold and goal, which, if achieved, would result in a significant improvement for the relevant application). Where multiple WMO application areas require observations of the same physical variable in the same domain, they generally have different requirements. The OSCAR/Requirements database contains technology-free requirements for each of the WMO application areas and is reviewed on a regular basis to ensure that it remains extant. Assessment of what is feasible compared with the requirements results in a gap analysis, which together with the results of impact studies and expert knowledge, forms the basis for "statements of guidance" for each application; these are concise summaries of the gaps and deficiencies in the current capability and inform decision makers toward the evolution of the observing system.
A fourth foreseen component, OSCAR/analysis, a collection of tools and services to support the gap analysis, is still in its infancy. At present, the status of the ocean observing system is assessed by the status of individual networks against networkbased metrics, e. g. , spatial coverage of Argo floats or drifting buoys. However, most users, and the above application areas, are primarily concerned with the availability of data on one (or more) variables, e. g. , surface air pressure and SST for NWP, wind and waves for maritime operations and coastal flood protection, SST and sub-surface SST for monitoring ocean heat content. Hence, there is an effort under the JCOMM OCG to develop variablebased metrics, which will be related to the user requirements of the appropriate application areas as defined within OSCAR.
WDQMS As noted earlier, the WDQMS will help assure end users that the observations are quality monitored, where problems are identified and addressed. It has three basic functions: quality monitoring, evaluation, and incident management. WDQMS will use OSCAR/Surface as the source of metadata that describes the expected accuracy of the observational data. It aims to provide information on availability, timeliness, and quality of observations to data providers enabling them to take corrective actions as necessary. Traditionally for marine observations under the WMO GOS, designated WMO monitoring centers that run global NWP models undertake the quality monitoring. Quality monitoring reports, e. g. , observation minus model background statistics for VOS and buoy data, for various marine meteorological variables (surface air temperature and humidity, surface air pressure, wind speed and direction, and SST) are routinely generated as a by-product of NWP data assimilation systems.
However, for subsurface temperature and salinity profile data, standard real-time quality control tests have been developed under the Argo program, and these tests are also applied to other profile data (e. g. , from ship-based CTD measurements and marine mammal-borne sensors) where these data are distributed in real-time (or near real-time). Similarly, quality control tests have been developed for dissolved oxygen and are being developed for other biogeochemical variables, which will ensure that any such data distributed on the WIS or available through network-based GDACs (Global Data Assembly Centers) is of a minimum quality. However, for climate and scientific applications the collected data are subjected to more stringent delayed-mode quality checks that can identify whether there are any sensor drifts or offsets that need to be corrected for.