dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). For the BGC-Argo bio-optical sensors, which are used to derive chlorophyll-a and POC, concurrent HPLC and POC shipboard sampling in the near-surface have been collected with all SOCCOM deployments. These are currently not GO-SHIP Level 1 measurements, but they have been critical for validation of the NASA algorithms for converting satellite ocean color to in situ chlorophyll-a and particulate organic carbon (POC)  (Haentjens et al. , 2017). GO-SHIP cruises, and cruises that are modeled closely on GO-SHIP, are by far the most helpful and highest quality for these validations. Measurements from marine mammals typically use the Conductivity-Temperature-Depth Satellite Relay Data Logger (CTD-SRDL), developed by the Sea Mammal Research Unit at the University of St Andrews. Marine mammals often sample remote, harsh environments, e.
g. , the sea-ice edge and the Antarctic continental shelf during winter. However, CTD-SRDL measurements have a random bias in the conductivity cell caused by the marine mammal interfering in the inductive field of the CTD-SRDL  (Böhme et al. , 2009). Several methods for correcting the individual offsets, which is assumed constant in space and time, have been developed (e. g. ,  Böhme and Send, 2005; Roquet et al. , 2011; Frazer et al. , 2018). Most of these rely on either little variability in deep ocean measurements from GO-SHIP or delayed mode Argo data (corrected with GO-SHIP data), implicitly placing GO-SHIP as the underlying provider of reference quality data for CTD-SRDL. Global Ocean Ship-Based Hydrographic Investigations Program provides a platform for assessing new sensors, including BGC sensors, mixing sensors, and biological sensors, inter alia.
For instance, to determine their characteristics, oxygen-sensing optodes have been deployed alongside conventional electrode oxygen sensors, allowing both to be compared with bottle oxygen titration data  (Sasano et al. , 2011). In addition, float CTDs can be strapped onto the shipboard CTD package for in situ evaluation of their sensors relative to reference quality shipboard CTD sensors. Given the importance of GO-SHIP as the provider of the high quality reference data needed by autonomous platforms and other components of the observing system, it is recommended that GOOS endorses GO-SHIP as the ocean reference data set, and that GO-SHIP data be given a unique identifier in the data system to enable users to identify it separate from other observing platforms or sensors.
Global Ocean Ship-Based Hydrographic Investigations Program is developing the protocols and methods to generate a data product that concatenates all occupations of individual sections into a time-series (Figure  5 ). Initial efforts are focused on CTD (temperature, salinity, and dissolved oxygen) data, with the plan to include GLODAP quality-controlled bottle data in future versions in collaboration with GLODAP. Global Ocean Ship-Based Hydrographic Investigations Program data held at CCHDO and national data repositories are included in the World Ocean Database (WOD;  Boyer et al. , 2013). Much of the nutrient, carbon and deep ( > 2000 dbar) physical data in this dataset originates from GO-SHIP cruises. The WOD is used by a large number of research institutions and meteorological and ocean operational centers for many purposes.
In addition, the SCOR International quality controlled ocean database (IQuOD) working group aim is to add value to the WOD by providing a consistent quality controlled subset of data, that has comprehensive meta-data information for use by climate and ocean science researchers and operational services. All gridded ocean climatology products [e. g. , World Ocean Atlas (WOA), CSIRO Atlas of Regional Seas (CARS), and Global Hydrographic Climatology (WOCEGHC)] are derived from WOD (and other data sources). Such climatologies represent a significant route by which users access GO-SHIP data. The above products mostly grid temperature and salinity, with some including dissolved oxygen and nutrients. Global Ocean Ship-Based Hydrographic Investigations Program data, largely via WOD and other databases, contributes to ocean state estimates and reanalysis products  (Balmaseda et al.
, 2015  includes an extensive list of products). GO-SHIP hydrographic data are also used in datasets such as EN4 and Coriolis Ocean dataset for Reanalysis (CORA), which provide higher quality measurements at a global scale for numerical modeling, data assimilation, and scientific studies. Such data assimilation efforts are used for the initialization of the current climate state from which forecasts are run. Products based on direct velocity measurements using S-ADCP and L-ADCP instrumentation are useful (e. g. ,  Johnson et al. , 2002) , and more are desired. However, the primary data management challenge at this time for ADCP data is to process and archive all of it, including essential metadata, in a single data system. This includes preservation of the WOCE ADCP datasets. The community, particularly that involved with mixing and turbulence calculations (e.
g. ,  Kunze et al. , 2006; Huussen et al. , 2012; Waterhouse et al. , 2014) , has a clear need for easy access to these velocity profile data and all repeats (e. g. ,  Kunze, 2017a ,b whose analysis relied on hydrographic data alone to achieve global coverage). Synthesis products of mixing-related quantities based on these repeats would be a useful adjunct to hydrographic data syntheses. Global Ocean Ship-Based Hydrographic Investigations Program must develop data assemblages by section and/or decade to increase the ease of access and use of all of its Level 1 observations. These data assemblages should follow standard data practices and be provided in a number of file formats that meet users' requirements. Funding of such activities should be a priority of the community. The use of GO-SHIP data in commonly used datasets enhances the use of the data.
9 At least two of the three parameters DIC, TAlk and pH are required (Dickson, 2010). 10 United States GO-SHIP tracks and links to all of its component datasets: http://usgoship. ucsd. edu. 11 nap et al. (1996). 12 Hooker et al. (2005). 13 N/A: Currently not available but highly desired. Frontiers in Marine Science | www. frontiersin. org.
" The complete algorithm used in MEDLINE database search is available in Data S1. Eligible studies were also sourced from the hand-searching of key journals, conference proceedings and other (non-Cochrane) systematic reviews and meta-analyses. No language or other search restriction was applied. Last literature search was performed on June 11, 2019. No time or protocol restrictions were implemented, and thus we included all RCTs providing mortality rates on the 3-month follow-up. Per study protocol we excluded from further evaluation all observational studies, case reports, case series, and studies not providing 3-month mortality rates. We also excluded studies using first generation thrombectomy devices and studies with possibility of enrolling patients without LVO. Reference lists of all articles that met the inclusion criteria and of relevant review articles were examined to identify studies that may have been missed by the initial database search.
We performed quality control for each eligible study with the Cochrane risk of bias assessment tool. Literature search, data abstraction, and bias identification were independently performed by 2 authors (AHK, KM) and all emerging conflicts were resolved after consultation of the senior author (GT). Using aggregate data from available RCTs we performed random-effects meta-analyses on the risk of 3-month mortality and the probability of functional improvement at 3 months in the ordinal scale between patients randomized to MT+BMT or BMT alone. Mortality rates in each group were calculated after implementing the variance-stabilizing double arcsine Freeman-Tukey transformation. Heterogeneity was assessed with the I 2 and Cochran Q statistics. Number needed to treat was calculated using the formula number needed to treat=1/[(1-Risk Ratio) x mortality rate in BMT alone group].
To foster multidisciplinary collaboration and avoid overlaps between research activities, the Jena Experiment has a coordinated system of project and paper proposals, which are centrally registered (see data curator in Database section below). Following a common proposal template, information is presented to all members in a standardized way. All members of the Jena Experiment have the possibility to comment on the project proposal (which can also be submitted by external scientists) within a time frame of two weeks. Before submitting the respective manuscript to a scientific journal, it is again sent to all Jena Experiment members. To foster the education of our PhD students, we will organize statistics and writing workshops (one per year). Additionally, we will organize a yearly workshop for the female scientists.
Database and international collaboration From the past Research Units, we have learned that collaborative research requires a wellfunctioning database. The culture of data sharing and joint analyses is a great strength of the Jena Experiment and has already resulted in important contributions to the field (e. g. ,  Scherber et al. 2010 , Allan et al. 2013 , Meyer et al. 2016 , Meyer et al. 2018). In addition, the Jena Experiment contributed data to impactful across-experiment syntheses (e. g. ,  Isbell et al. 2011 , Isbell et al. 2015 , Lefcheck et al. 2015 , Craven et al. 2016 , Guerrero-Ramírez et al. 2017 , Craven et al. 2018 ) and has established the database infrastructure as well as data use and publication policies to facilitate similar work in the future. These measures have inspired other German research consortia (e.
g. , the Biodiversity Exploratories, BEF China) that use very similar databases, data use and publication policies, as well as project and paper proposals (see below). Further, the Jena Experiment is regularly disseminating data to the scientific community. For instance, the Jena Experiment has published several thousand datasets at Pangaea and the iDiv Data Repository. The Jena Experiment has many collaborations in Germany and beyond, and researchers from the Jena Experiment have contributed to many syntheses and meta-analyses of BEF relationships in the last decade. Strong connections have been established to the other two long-term grassland biodiversity experiments in Cedar Creek (e. g. ,  Isbell et al. 2011 , Reich et al. 2012 , Thakur et al. 2015 , Craven et al. 2018) , and more recently to the global network of tree diversity experiments  (TreeDivNet; Guerrero-Ramírez et al.
2017) , BEF-China  (Schuldt et al. 2019) , and the Biodiversity Exploratories  (M. Jochum et al. , unpubl. data). The planned collaborations with the requested Mercator Fellows will further strengthen these international collaborations, e. g. , by closely linking to the other two running long-term grassland biodiversity experiments, BioCON and BioDIV  (Reich et al. 2012 ). In the proposed Research Unit, database infrastructure support will be provided by iDiv, including workshops on data management and open data, maintenance of database (helpdesk data upload; data upload and publication in iDiv Data Repository), archiving and back-up, hard-and software updates, structural adaptations for new experimental designs (e. g. , Ecotron), and transition of old and new databases. In addition, we request a data curator who will be responsible for data quality control and scientific computation help for PhD students, compliance with reproducible science principles (e.
Requested budget for coordination of the Research Unit Description of how joint objectives and the joint work programme will be implemented in the coordination project As outlined above, the described scientific objectives will be achieved via a plethora of communication and interaction channels that have proven successful in facilitating integrative BEF research. Regular meetings (Jena Retreats) provide the basis for mutual trust and collaboration. We will use multiple communication channels to guarantee the highest level of transparency, including regular email updates, webpage, Twitter, and Google Scholar accounts, a common database, a clear data use policy, and a common project-and paper proposal system. These means as well as central coordination of experimental setups and sampling campaigns will be strongly supported by SP Z1 (see SP Z1 proposal).
Acknowledgements We thank the eight reviewers for their very helpful feedback on our research plan and the positive evaluation. We thank the  Deutsche Forschungsgemeinschaft (DFG ;  German Research Foundation ) for financial support ( FOR 456 ,  FOR 1451 ,  FOR 5000 ) as well as the  Friedrich-Schiller-University Jena, Max-Planck-Institute for Biogeochemistry Jena, Leipzig University , and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ), for financial and logistical help with the set-up and maintenance of the  Jena Experiment. We also thank the gardeners for their great help maintaining the experimental plots and  Ernst-Detlef Schulze  for initiating the  Jena Experiment. Moreover, we thank  Anja Vogel  for coordinating the setup of the Field Experiment as well as  Kathrin Greyer  and  Svenja Haenzel  for her help preparing this proposal and the on-site evaluation.
In October 2017, the Sentinel-5 Precursor (S5p) mission was launched, carrying the TROPOspheric Monitoring Instrument, TROPOMI, which provides a daily global coverage at a spatial resolution as high as 7 km x 3. 5 km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, bringing up significant new components to the scientific knowledge of atmospheric processes. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work the TROPOMI Near-Real Time, NRTI, and Offline, OFFL, total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC).
Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, that form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith and viewing angles, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 -1.
TROPOMI has a local overpass time of 13:30 UTC, a ground pixel size of 3. 5 km x 7 km for total ozone columns (TOC) and all major atmospheric gases retrieved from the UV/VIS, a swath of 2600 km and provides daily global coverage with ~14 orbits per day. The TROPOMI instrument and its pre-launch calibration techniques are thoroughly described by  Kleipool et al. , 2018. The mission products are disseminated to both operational users, such as the Copernicus services, National Numerical Weather Prediction Centres, value-adding industry, and, naturally, the scientific community. Some studies utilizing TROPOMI data have highlighted its high spatial resolution and spectral accuracy for various species, e. g. nitrogen dioxide  (Griffin et al. , 2019) , sulphur dioxide  (Theys et al. , 2019) , carbon monoxide  (Borsdorff et al.
, 2018) , methane  (Hu et al. , 2018) , solar-induced chlorophyll fluorescence  (Köehler et al. , 2018) , to name a few. With respect to TOCs,  Inness et al. , 2019, show  first global maps for one year of TROPOMI observations, as well as the first efforts to assimilate the TOCs into the operational data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS). The aim of this work is to fully characterize the TOC product from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5p) satellite regarding biases, random differences and long-term stability with respect to ground-based TOC observations. In this context, the accuracy and long-term stability of TROPOMI TOC against Product Requirements will be verified via comparisons to both ground as well as other, already established, space-borne missions.
, 2018) , to assure the quality of the NRTI data, the following quality checks are used to remove any outliers of the TROPOMI TOC data. Data are only used if: • the TOC value is positive but less than 1008. 52 DU, • the respective ozone effective temperature variable is greater than 180 K but less than 280 K and • the fitted root mean square variable is less than 0. 01. NRTI Data are available through the Sentinel-5p Pre-Operations Data Hub (https://s5phub. copernicus. eu/) and the time periods and processor versions used in this work are listed in Table  1.
The OFFL TOC product The TROPOMI OFFL TOC product relies on the operational implementation of the GODFITv4 (GOME-type Direct FITting) algorithm, which is a direct-fitting algorithm developed to retrieve in one single-step total ozone columns from satellite nadirviewing instruments. Simulated radiances in the Huggins bands (fitting window: 325-335 nm) are directly adjusted to the observations by varying a number of key parameters describing the atmosphere. In particular, the state vector includes among others the total ozone, the effective scene albedo and the effective temperature. This approach, more physical-sound than the usual DOAS technique, provides more accurate retrievals in extreme geophysical conditions (large ozone optical depths). GODFITv4 is also the baseline to produce the Copernicus C3S and ESA CCI climate data records from the different sensors GOME, SCIAMACHY, GOME-2A/B, OMI and OMPS.
More details on the algorithm and on the quality of the data sets can be found in  Lerot et al. (2014)  and  Garane et al. (2018). OFFL TOC data are available through the Sentinel-5P Expert Users Data Hub (https://s5pexp. copernicus. eu/) and the Sentinel-5P Pre-Operations Data Hub (https://s5phub. copernicus. eu/) and the datasets used here are listed in Table  1. The data filtering was applied following the recommendations of the S5p Mission Performance Centre Readme Document for the OFFL Total Ozone product  (Lerot et al. , 2018) , keeping data only if all of the following criteria are met: • the respective ozone effective temperature variable is greater than 180 K but less than 260 K, • the ring scale factor variable is positive but less than 0. 15 and • the effective albedo is greater than -0.
g. Kerr et al. , 1988 Kerr et al. , , 2010)). Note that instrument uncertainties vary from site to site depending on the instrument state, calibrations history and other factors  (Fioletov et al. , 2004). According to  Hendrick et al. (2011)  the total uncertainty of the SAOZ measurements is of the order of 6 %, which contains the systematic uncertainty of the absorption cross sections (3 %). The random uncertainty of SAOZ spectral analysis is less than 2 %, going up to 3. 3 % when the random uncertainty on the air mass factor, mainly impacted by clouds, is added  (Hendrick et al. , 2011). Another, possibly important source, of bias between the different datasets discussed in this paper is the use of different ozone absorption cross-section coefficients; while the Dobson and Brewer TOC algorithms are based on the traditional  Bass and Paur (1985) , BP, ozone absorption cross-sections, the TROPOMI NRTI TOCs are extracted using the so-called 'Brion-Daumont-Malicet', BDM, cross sections  (Daumont et al.
As it is reported, this is caused by a misalignment of bands 3-4 (450 pixels per scanline) and 6 (448 pixels per scanline), which led to the application of a shift of two detector pixels between the two bands. Therefore, due to the lack of cloud information for the first two pixels, the respective data could not be analyzed. Daily values of TOC retrieved from the WOUDC and the NDACC databases were widely used in previous studies for GOME2/Metop  (Koukouli et al. , 2015a) , IASI/Metop  (Boynard et al. , 2018) , OMI/Aura  (Garane et al. , 2018)  Canadian Brewer Network are also used in this study. Thus, the effect of the time difference of the sensing between satellite and ground-based measurements had to be investigated. For this purpose, the mean percentage differences were computed for all co-located measurements with maximum temporal differences (Δtmax) varying between 5 and 60 minutes, keeping the search radius limit to 10 km.
Validation of the NRTI & OFFL TOC After having all the necessary co-location criteria determined, the validation of one full year of available satellite data is discussed in this section. Specifically, the TROPOMI TOC OFFL and NRTI products are validated via the statistical analysis of their comparisons to all the afore-mentioned GB instruments. Emphasis will be given to the quantification of biases, seasonal and/or spatial dependences, instrument mode and/or geometry dependences (SZA, scan mode, etc. ), dependences on atmospheric conditions such as cloud parameters, effective temperature and ground albedo. The TROPOMI TOCs will also be compared to other, long-term and already validated, space-based TOC observations such as the GOME2/MetopA and MetopB, and OMPS/NPP-Suomi operational datasets, using the same ground-based observations as reference.
, was found either and no unexpected effect of other input parameters (such as total 10 air mass factor), fitting statistics or measurement constants (like the CCD pixel of the sensor), was seen. The effective temperature is the only exception in the general very smooth picture, which when being less than 210 K or higher than 250 K it causes biases of up to ± 4 %, especially in the Dobson comparisons where it has a stronger effect as is well-known  (Koukouli et al. , 2016). Finally, in Table  2  the overall global statistics, as well as the latitudinal statistics for the two TOC products and their comparisons to Dobson, Brewer and SAOZ GB measurements, are summarized. The mean bias of each dataset is listed in this table, along with the mean standard deviation, which is the mean of the standard deviations of the (global or latitudinal) means.
3. Ιn the latitudinal dependency of the comparisons, seen in Figure  15 , a very good agreement between the three sensors is obvious in the NH, with deviations of up to 1 %. The only exception is the highest latitude bin of the Dobson comparisons, as it was also seen in Figure  7 (a). One would expect that since the NRTI product calculation is based on the GDP 4. x algorithm, the differences between the three sensors should be minor. However, the two algorithms (GDP4. 8 and NRTI) are different in some aspects such as the surface albedo climatology used for the TOC retrievals, which is the main reason for the deviations discussed above. The other important updates are briefly discussed in Sect. 2. 1. 1 and are summarized in Table  3. Furthermore, it was seen (not shown here) that the deviation between the two algorithms in this particular latitude bin is almost eliminated when TROPOMI data acquired during the commissioning phase of its operation are excluded from the data set.