dataesr/scientific-paragraphs-categorization huggingface dataset:
SOCAT data assembly 3. 1 Data sources and instrumentation SOCAT includes 6. 3 million f CO 2 data points measured in all ocean areas from 1968 to 2007. Most of these data were gathered from the online sources at CDIAC (30 % of the cruises) and PANGAEA (10 %), as well as from institute and project websites (37 %). The remaining cruises (23 %) were obtained directly from the data originators. Almost half of the cruises (45. 7 %) originated in the USA. Other significant contributors are based in Japan (20. 1 %), Norway (9. 6 %), the United Kingdom (7. 4 %), Germany (5. 8 %), France (4. 5 %), Belgium (2. 4 %), Canada (1. 6 %), Spain (1. 5 %), Australia (1. 2 %) and the Netherlands (0. 3 %). The data in SOCAT are a synthesis of 4 decades of seagoing fieldwork by numerous scientists from 12 countries.
Various instruments have been used to obtain these data and only the basic principles will be summarised here. Further information is available in the metadata, which accompany individual cruise files at PANGAEA (doi:10. 1594/PANGAEA. 769638) (Sect. 5. 2). The seawater f CO 2 values included in SOCAT have been measured according to one of the following two principles: (1) analysis of the CO 2 content in an air sample in equilibrium with a large volume of seawater or (2) calculation of the seawater f CO 2 from the colour response of an acidbase indicator dye (sulfonephtalein) in contact with seawater across a CO 2 permeable membrane. The analysis of the CO 2 content in an air sample in equilibrium with a large volume of seawater is recommended in the standard work by  Dickson et al. (2007).
Live Access Server for quality control The Live Access Server is a web server designed by NOAA PMEL (National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory) to provide access to geo-referenced scientific data (http://ferret. pmel. noaa. gov/ LAS). Cruise data and metadata were ingested into a relational database and made available to the regional teams for evaluation through a version of the LAS, which had been enhanced with SOCAT quality control tools. Contents of the database included recommended f CO 2 values, ancillary parameters, cruise metadata, and reference variables drawn from other sources (Sect. 5. 4). The LAS enabled QC operators to query the data collection using criteria of region, time period, seasonality, cruise and ship identifiers, and ranges of data values.
SOCAT products and tools 5. 1 SOCAT cruises, versions, and time stamps SOCAT data are publicly available via the SOCAT website (www. socat. info) as individual cruise data files (SO-CAT version 1. 4) (Sect. 5. 2) and as regional and global, concatenated files (SOCAT version 1. 5) (Sect. 5. 3). SO-CAT versions 1. 4 and 1. 5 include all cruises with a cruise flag A, B, C or D. A table of these cruises is available at doi:10. 1594/PANGAEA. 769638 and provides information about the investigator, research vessel, Expocode, original cruise naming, metadata (as reported by the investigator), and temporal and geographical coverage. Through PANGAEA SOCAT is fed into the ICSU World Data System (WDS). The Global Earth Observation System of Systems (GEOSS), which is being built by the Group on Earth Observations (GEO), makes SOCAT available to other research communities.
The individual cruise data files (version 1. 4) record observation time stamps at a resolution of integer minutes, rounding off the seconds, when they were available. Some cruises have multiple recommended f CO 2 values for a given time stamp (around 5 % of the observations). Individual cruise data files (Sect. 5. 2) contain all recommended f CO 2 data, including multiple values per minute. However, handling multiple entries for the same time stamp can be problematic for some software programs. The SOCAT global group decided to average multiple entries within a given minute for the regional and global synthesis files (Sect. 5. 3) as a pragmatic solution to this issue. Table  5  lists the contents of the SOCAT files in versions 1. 4 and 1. 5. Matlab code by Pierrot and Landschützer for reading these files is available via the SOCAT website or directly at CDIAC (http://cdiac.
Individual cruise data files (version 1. 4) Individual cruise data files (version 1. 4) with cruise flags A, B, C and D are available via PANGAEA (doi:10. 1594/PANGAEA. 767698). These cruise data files include all recommended f CO 2 data with WOCE flags 2 (good), 3 (questionable) and 4 (bad), without listing these WOCE flags. Cruise data files archived at PANGAEA have not been averaged to remove multiple entries per minute (Sect. 5. 1). The individual cruise data files provide access to the metadata, the original CO 2 parameter(s) (as reported by the investigator), which were used to (re-)calculate f CO 2 (Sect. 3. 3), and the (re-)calculated and quality controlled f CO 2 data. The files contain these additional parameters: WOA salinity  (Antonov et al. , 2006) , NCEP/NCAR sea level pressure  (Kalnay et al.
SOCAT global and regional files (version 1. 5) Regional and global concatenated files (version 1. 5) have been merged from the individual cruise data files for a subset of SOCAT parameters (Table  5 ). These concatenated files only contain recommended f CO 2 data with a WOCE flag 2 from cruises with a flag A, B, C or D. Table  5  lists the parameters in these regional and global synthesis files. Some changes have been applied relative to SOCAT version 1. 4 (Sects. 5. 1 and 5. 2). Notably, multiple entries with the same time stamp were averaged for the global and regional synthesis files (Sect. 5. 1). Additional parameters have been added to the regional and global, concatenated files. These include Julian day (day of year), interpolated atmospheric xCO 2 extracted from GLOBALVIEW-CO2 (2008), WOA salinity, NCEP/NCAR sea level pressure and ETOPO2 bathymetry.
The global and regional files specify which reported CO 2 variable was used for (re-)calculation of recommended f CO 2 (Sect. 3. 3; Table 5). Every line of the concatenated files contains a doistring, which provides a link to the individual cruise data file with the original CO 2 parameter(s) and metadata at PANGAEA (Sect. 5. 2). The regional and global concatenated files (version 1. 5) are publicly available as "compressed zip" text files via CDIAC (http://cdiac. ornl. gov/ftp/oceans/SOCATv1. 5/), in Ocean Data View (ODV) format (http://odv. awi. de/en/data/ ocean/socat v15 fco2 data/) and via the interactive Cruise Data Viewer (Sect. 5. 4). NetCDF files  (Eaton et al. , 2011)  will be made available in the future. The text files exist as one very large global file, and as subset files per region, with no overlap between the regions.
Cruise Data Viewer (version 1. 5) The LAS Cruise Data Viewer provides interactive access to SOCAT version 1. 5 on a Live Access Server. It provides all of the output capabilities described in Sect. 4 as tools for the SOCAT QC-ers, except for the ability to enter QC flags and comments. The Cruise Data Viewer also supplies variables from other sources that provide scientific context useful to users of the f CO 2 data: atmospheric xCO 2 values interpolated from GLOBALVIEW-CO2 (2008), WOA 2005 salinity, NCEP/NCAR sea level pressure values  (Kalnay et al. , 1996) , and bathymetry from  ETOPO2 (2006). The Cruise Data Viewer allows the inclusion of WOCE flag 3 (questionable) or 4 (bad) data when viewing or downloading data. When subsets are downloaded from the Cruise Data Viewer, each data line contains a doi-string that links di-rectly to the relevant cruise data file with its original reported CO 2 parameters at PANGAEA.
Gridded Data Viewer (version 1. 5) The interactive LAS Gridded Data Viewer enables users to explore the gridded SOCAT fields. The viewer displays maps and time series for the specific region or period selected. Sequences of fields can be viewed as animations. Simple statistics such as means, extremes, variance and counts, may be requested of the data. By requesting counts of the number of observations and cruises, a user is able to explore the global coverage of the SOCAT collection. Figure  4  obtained by this means, illustrates the north-south distribution of cruises in the years 2000 through 2007. The gridded viewer also supplies 1 • latitude by 1 • longitude marine surface variables from ICOADS (2008) that provide useful scientific context when exploring f CO 2 : surface air temperature, sea level pressure, sea surface temperature, and surface wind speed.
Lessons learned SOCAT has taken four years to be put together and has been a large, international, collaborative effort of the marine carbon  research community. SOCAT version 1. 5 is the culmination of much hard work in data collection, data assembly and quality control by many seagoing marine carbon scientists around the world. Lessons learned and improvements for future SOCAT releases have been discussed at the Surface Ocean CO 2 Datato-Flux workshop  (IOCCP, 2013). The lessons include a strong need for automating SOCAT with respect to data submission, metadata submission and quality control. The automation and other improvements will reduce the amount of work required for creating SOCAT data products and SO-CAT quality control, while at the same time speeding up the whole process with the aim to provide regular updates.
The SOCAT global group, upon consultation with regional group leaders, has decided to start work on SOCAT version 2, while in parallel automating SOCAT for version 3. Data submission to SOCAT version 2 was closed on 31 December 2011. SOCAT version 2 products will report time in seconds as reported in the original data files to remove the need to calculate averaged data. Regular SOCAT releases are envisaged, e. g. , every two years from SOCAT version 3 onwards. Such regular future SOCAT releases will require sustained funding for key players. Colleagues are strongly encouraged to make public their surface water f CO 2 data and accompanying documentation from the global oceans and coastal seas, preferably via CDIAC (http://cdiac. ornl. gov/oceans/submit. html) for inclusion in future SOCAT releases.
Automation of SOCAT procedures Automation of the submission of data and metadata will include prompt feedback to the data originator on unrealistic data and property-property plots of the data, such that the data originator can carry out primary and initial secondary quality control. Such automation will facilitate harmonisation of the data for SOCAT and will strongly reduce the number of cruises suspended from SOCAT during secondary quality control. In the future, new cruises will be added to the LAS at regular (e. g. , two monthly) intervals, enabling QC operators to carry out regular SOCAT QC. The Live Access Server will be modified to automatically generate typical property-property plots for secondary QC. The LAS will be enhanced with features to enter cruise flags and QC comments for multiple cruises (e.
Emilie Brévière (SOLAS executive officer) and  Lisa Maddison  (IMBER deputy executive officer) have strongly encouraged SOCAT. Support for SOCAT has been received from the  Bjerknes Centre for Climate Research , the  University of Bergen, Uni Research (Norway) , the  US National Oceanic and Atmospheric Administration , the  University of Washington ,  Oak Ridge National Laboratory (US) , the  University of East Anglia (UEA, UK) ,  PANGAEA -Data Publisher for Earth & Environmental data (Germany) , the  Alfred Wegener Institute for Polar and Marine Research (Germany) , the  Centre National de la Recherche Scientifique (France) , the  Research Council of Norway  ( CARBON-HEAT ), the  US National Science Foundation  ( OCE-1068958 ), the international Scientific Committee on Oceanic Research (SCOR,  US ,  OCE-0938349 ), the  European Union  integrated projects CarboOcean ( GOCE 511176-1 ) and  CarboChange  ( FP7 264879 ), the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ; funded by the  Natural Environment Research Council , the  Department for Energy and Climate Change  and the  Department for Environment, Food and Rural Affairs)  and the  UK National Centre for Earth Observation.
Altekin et al. [3]  defined and solved the profit-oriented DLBP. The problem was modeled via a mixed-integer programming formulation and its solution simultaneously determined the number of stations and cycle time along with the assignment of the tasks to the stations. Upper and lower bounding schemes were also developed. Koc et al. [7]  proposed two exact (MIP and DP) formulations to solve DLBP with the objective of minimizing the number of stations. They used an AND/OR graph to model EOL product data and showed that the use of such a graph allowed obtaining better solutions in comparison with a single precedence diagram. Altekin and Akkan  [2]  considered task-failure driven rebalancing of disassembly lines. A mixed-integer programming based predictive-reactive approach was proposed. In the first step, a predictive balance was created and then, in the second step, given a task failure, the tasks of the disassembled product with that task failure were reselected and re-assigned to the stations.
Numerical Results The program (SOCMIP) was implemented in Microsoft Visual C++ 2008 and ILOG CPLEX 12. 4 was used to solve the model on a PC with Intel(R) Core(TM) i5-2400 CPU 3. 10 GHz and 8Go RAM. It has been applied to the problem instance illustrated in Fig. 1  and to 5 available in the literature benchmark problems containing process alternatives for disassembly. The names of the problem instances were respectively composed of the first letters of authors' names and year of publication, i. e. BBD12  [5] , KSE09  [7] , L99a and L99b from  [8] , MJKL11  [9]. BBD13 corresponds to the piston and connecting rod product instance, see Fig. 1. The input data for each problem instance is given in Table  1. The columns 'AND-relations' report the number of disassembly tasks with no successor in column 0, with one AND-type arc in column 1, and with two ANDtype arcs in column 2, column 'arcs' gives the total number of AND-type and OR-type arcs.
Conclusion In this paper, partial profit-oriented disassembly line balancing problem in the presence of hazardous parts was studied under uncertainty. A second-order cone mixed integer program was developed. Disassembly task times were assumed mutually independent random variables with known normal probability distributions, where cycle time constraints were to be jointly satisfied with at least a probability level fixed by the decision maker. To solve the problem, the interior point algorithm and CPLEX solver were used. The solution method was evaluated on a set of 6 problem instances taken from the literature. All instances were solved in less than 1 second. Our future objective is to develop an exact solution algorithm for the problem considered based on branch & cut method and compare it to interior point CPLEX algorithm.
There are a number of web servers allowing NMA on atomic resolution structures: ElNemo (9), AD-ENM  (10) , WebNM@ (11), ANM  (12) , NOMAD-ref  (13)  and so forth. The other reported applications are web-based databases with pre-computed normal modes and animations of some of EM structures from the EMDB database, and they do not allow the user to perform NMA on his/ her own EM volumes [e. g. emotion (4) and CDDB  (14) ]. To tackle this problem, we developed a user-friendly web server 3DEM Loupe that allows an automatic NMA of input EM volumes. The workflow comprises a conversion of the input volume into a pseudo-atomic structure, NMA of the pseudo-atomic structure and an animation of the computed modes, and it allows the user to download the computed pseudo-atomic structure, modes and animations.
3DEM LOUPE USE CASE AND OUTPUT DESCRIPTION A friendly interface has been designed to guide users step by step through the analysis. Results can be downloaded in different formats (e. g. PDB and other text files, SPIDER volumes or MP4 videos) for offline analysis, but they can also be explored dynamically with different visualizers that allow users to interpret the results obtained at each step of the analysis pipeline. The following section will describe a use case by analyzing citrate synthase (CS) volume, as well as describe the user interface of 3DEM Loupe. Dynamics of the open form of CS were analyzed in a previously published study (3) by NMA of the atomic resolution structure with the PDB code 5CSC [6606 atoms  (19) ] and by NMA of synthetic EM volumes obtained by converting the 5CSC structure into a density volume and its low-pass filtering.
That study has shown that the best overlap between the conformational change experimentally observed for CS [change from the open form  (19)  to a closed form  (20) ] and the computed normal modes was obtained for mode 9 for both atomic and pseudo-atomic modes  (3). Here, we show that 3DEM Loupe reproduced these previously reported results (Figure  2 ). The input volume for uploading on the 3DEM Loupe site was obtained from the 5CSC structure that was converted into volumetric data [size: 64 3 voxels, voxel size: (2 A ˚)3 ] and low-pass filtered at the resolution of 10 A ˚. In a first step, the slices along the z-axis and three orthogonal projections are shown, and the input volume is also visualized with Astex viewer. In this step, users can select a threshold below which the densities in the volume are set to 0 (threshold of 0.
TECHNICAL DETAILS 3DEM Loupe core relies on system calls to Xmipp (in C+ +)  (21)  for volume-to-pseudo-atoms conversion, A Ruby wrapper processes all data and creates a script to run the core program in our cluster server. Client-server architecture has been used to decouple the front-end from the heavy computational code that runs on a different server. We use a cluster of six new generation servers, each one containing two Quad-Core Intel Xeon 64 bits processors that are able to handle a large number of simultaneous jobs. 3DEM Loupe has been running for a few months, experiencing >100 submissions. Extensive tests have been carried out in different web browsers using synthetic and real data sets for which the outcome of the software is known.
FUNDING  CNRS (France)  and  CSIC (Spain) international cooperation program  [ PICS 2011  to C. O. S. S. and S. J. ];  French National Research Agency  [ ANR-11-BSV8-010-04  to S. J. ];  Spanish Minister of Science and Innovation  [ BIO2010-17527 ];  Government of Madrid (CAM)  [ P2010/BMD-2305 ];  Juan de la Cierva research program  (to R. N. C. ); Ramo´n y Cajal fellow (to C. O. S. S. ). Funding for open access charge: Research projects.
ClearASIL The high SNR mean line-profiles (in this case obtained using the LSD technique) are then input into ClearASIL. The code operates on the basis that RV variations of a time series of line-profiles are either due to spots only or due to both spots and an orbiting planet. ClearASIL processes the lineprofiles according to both these theories and then tests to see which assumption best removes the spot features. This is a three stage decision process that is outlined in more detail below. The code iterates over this process using the resulting average line-profile from the best spot removal method in the next iteration. At the end of each iteration a goodness of fit test (χ 2 ) between the resulting line-profiles from each fitting method and their average is calculated to determine the correct fitting technique.
If the planet has zero orbital eccentricity then the RV variation of the star over time will be a smooth sinusoid. The majority of hot-Jupiter planets have been found in circular orbits which is thought to be a result of short circularization time-scales for these close orbiting planets  (Jackson et al. 2008). As the work in this paper concentrates on detecting hot-Jupiter planets then a valid approach in our method is to assume zero orbital eccentricity. With this assumption in mind the code generates sinewaves with various periods, amplitudes and phase offsets. Shifting the line-profiles according to each sinewave and measuring the goodness of fit between these shifted lines and the average determines the best model planet for the data. It is important to remove the planet signature at this stage of the fitting procedure so as to enable accurate removal of any spot features present in the subsequent steps.
This method utilises the approximation that spot features present in the residuals are Gaussian-like in shape as discussed by Collier  Cameron et al. (2002). The residuals are scanned for any peaks above this noise level and these features are fitted with a Gaussian using the Levenberg-Marquardt technique. The code also checks the width of these features to ensure spikes in noisy data are not wrongly treated as spot features. This enabled the spot bumps to be isolated from the noise features. These isolated spot bumps were then subtracted from the individual line-profiles to generate a cleaner data set.
3. The model spectrum covers the wavelength range, 4500 to 7000 Å similar to the typical usable wavelength range of spectra taken with an optical spectrograph such as HARPS or FIES. We have added photon noise assuming that the SNR is proportional to the square root of the flux. Since our model spectra are normalised, in order to determine the flux as a function of wavelength we have performed a cubic spline fit to a synthetic spectrum. The Pollux database provided the synthetic spectrum, of similar spectral type to our model star. This enabled to correct relative photon noise to be calculated. All the models described in this paper have been given a peak SNR of 100.
For this model star the code found correctly that the best fit for the line-profiles was the spot only fit and was able to effectively clean the true spot features from the line-profiles. The χ 2 levelled off at the 6 th iteration and the resulting RVs showed no realistic planet signature. The resulting RVs had an rms of 7. 45±3 ms -1 , showing that a 96% reduction in the stellar noise was achieved. Next we tested the case when a relatively small planet signature (compared to the stellar jitter) is present. We placed a 1 MJ planet on a 4 day circular orbit around the same model star and again generated 19 line-profiles that evenly covered 1 stellar rotation period. The RV semiamplitude caused by this planet is 48. 7 ms -1 , which given the previous test should be discernible if our spot removal algorithm does not overly affect the planet signature.
We note that, in this test, the stellar rotation period and planetary orbital period are similar. Thus, for a significant fraction of our simulated observations, the RV jitter due to the spot, dominates over the weaker planet signature and is phased similarly (as can be seen in Fig. 7 ). For the first iteration of ClearASIL the spot only fit was found to best remove any spot features from these lineprofiles. However, in subsequent iterations, the code cleaned the line-profiles using the combined planet and spot fit. The χ 2 reached a minimum at the 10 th iteration and the results showed there to be a periodic RV variation with a semiamplitude of 49. 8±1. 4 ms -1 which closely matches the RV signal of the injected planet (see right panel of Fig. 7 ). Fitting these RVs with a sine wave reveals a 1.
03±0. 05 MJ on a 4. 08±0. 115 day orbital period, indeed confirming the results are consistent with the same as the planet injected into this model. This simple test case shows that ClearASIL was not only capable of removing the majority of a large spot from the line-profiles but also has the ability to reveal a planet signal that is partially hidden in the RV data. Having shown that the code was able to impressively uncover the 1 MJ mass signal hidden in the stellar noise we decided to further test the ability of ClearASIL. In the next case we investigated the impact the phase of the injected planet signal had on the planet signature that could be uncovered from the noise. Again we used the same 1 spot model star with a 1 MJ planet on a 4 day circular orbit. The phase of the planet compared to the stellar rotation phase was varied between 0 and 1 in steps of 0.