dataesr/scientific-paragraphs-categorization huggingface dataset:
The blue and red simulations include background concentrations for 129 I (2. 5 × 10 7 at kg -1 ; Edmonds et al. , 2001 ) and 236 U (10 × 10 6 at kg -1 ; e. g. Christl et al. , 2012)  due to the global fallout. On the black y axis are also the radionuclide concentrations measured in 2014 that correspond to the bottom sample collected in GEOVIDE station 69. Earlier data on 129 I concentrations at similar locations were reported for 1993 by Edmonds et al. (2001); for 1997   for  , 1999  for   and 2001 for by Smith et al. (2005);); for 2003  for  , 2005 for and 2009 for by Orre et al. (2010)) ; and for 2012 and 2013 bySmith et  al. (2016).
Acknowledgements. We are grateful to the captain and crew of  R/V Pourquoi Pas ? as well as the technical team for their work at sea ( Pierre Branellec ,  Floriane Desprez de Gésincourt ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour ,  Olivider Ménage ,  Fabien Pérault , and  Emmanuel de Saint-Léger ). A big thank you is extended to  Yi Tang  for help with sampling and to  Anita Schlatter  for assistance in the laboratory. This manuscript has been notably improved thanks to the constructive comments from two anonymous reviewers. We give thanks to  Jan Lukas Menzel  and the group led by  Eric Achterberg  ( GEOMAR, Kiel ) for providing the FISH device to collect surface seawater. The work by  Catherine Schmechtig  with the LEFE CYBER database management is also acknowledged.
Some of the 129 I data discussed from the literature were kindly provided by  Vasily Alfimov ,  Henrietta Edmonds  and  John Norton Smith. Several figures were created using Ocean Data View (Schlitzer, 2017). We would also like to thank  Natalie Dubois  and  Alfred Lück  for providing the laboratory space and assistance at  EAWAG. The GEOVIDE cruise was funded by the  French National Research Agency  ( ANR-13-BS06-0014 ,  ANR-12-PDOC-0025-01 ), the  French National Center for Scientific Research (CNRS LEFE CYBER) , the  LabexMER  ( ANR-10-LABX-19 ), and Ifremer. This work was funded by the  Ministerio de Economía y Competitividad of Spain  ( MDM2015-0552 ), the  Generalitat de Catalunya  ( MERS 2017 SGR-1588 ) and consortium partners of the  ETH Zurich Laboratory of Ion Beam Physics (EAWAG, EMPA, and PSI).
Maxi Castrillejo  was funded by a  FPU PhD studentship  ( AP-2012-2901 ) from the  Ministerio de Educación, Cultura y Deporte of Spain  and the  ETH Zurich Postdoctoral Fellowship Programme  ( 17-2 FEL-30 ), co-funded by the  Marie Curie Actions for People COFUND Programme. Núria Casacuberta 's research was funded by the  AMBIZIONE grant  ( PZ00P2_154805 ) from the  Swiss National Science Foundation. Maribel I. García-Ibáñez  was supported by the  Spanish Ministry of Economy and Competitiveness  through the  BOCATS  ( CTM2013-41048-P ) project co-funded by the  Fondo Europeo de Desarrollo Regional   2014-2020  (FEDER). Edited by: Catherine Jeandel Reviewed by: two anonymous referees.
On these axes are the blue and red lines that represent two versions of the radionuclide input function at 60 • N after dilution (DF = 50 and 30) and application of a time delay (6 and 14 years) that corresponds to the transport from 60 • N to the central Labrador Sea. The blue and red simulations include background concentrations for 129 I (2. 5 × 10 7 at kg -1 ; Edmonds et al. , 2001 ) and 236 U (10 × 10 6 at kg -1 ; e. g. Christl et al. , 2012)  due to the global fallout. On the black y axis are also the radionuclide concentrations measured in 2014 that correspond to the bottom sample collected in GEOVIDE station 69. Earlier data on 129 I concentrations at similar locations were reported for 1993 by Edmonds et al. (2001); for 1997   for  , 1999  for   and 2001 for by Smith et al. (2005);); for 2003  for  , 2005 for and 2009 for by Orre et al.
Rutile Chemical Composition The crystals are typically zoned on a scale of the order of 10 µm or less, precluding measurement of their chemical composition by EPMA. For this reason, the chemical composition was measured (including oxygen) by SEM-EDS on the basis of the back-scattered images. Sixty-one crystals from 12 samples were studied in great detail, and a selection of results is presented in Table  1  (the whole set of data is provided in Supplementary Data Table  S1 ). Structural formulae were calculated on a 3 cation basis, to conform to the general formula AB 2 O 6. With an average 6. 04 apfu (Atom Per Formula Unit) the calculated structural O is found to be very close to 6, attesting to the good quality of the analyses. The Panasqueira rutile systematically contains Fe, V, Sn, Nb, Ta and W, all with highly variable contents, in part because of sector zoning effects (see below): Fe from 0.
Acknowledgments: This work benefited from the analytical capabilities of the  GeoRessources Laboratory (Université de Lorraine, CNRS, CREGU) , and the authors gratefully acknowledge  Olivier Rouer  and  Lise Salsi  for their technical help. We thank  Isabella Pignatelli  for her assistance in crystallographic approaches. We are most grateful to  Beralt Tin  and  Wolfram S. A. for permitting access to the Panasqueira underground mine. Three anonymous reviewers are warmly thanked for their constructive criticism and help in improving the manuscript.
Collecting historical observations is a tremendous effort, which can only be undertaken through close collaboration between weather and climate data centers, satellite agencies, reanalysis producers, and individual scientists. Reanalysis producers share their observation databases and thus build on each other's efforts. However, each reanalysis project also has its specific objectives and technical restrictions or opportunities. Particularly when going back in time, not all observations are easily accessible. A large fraction of historical meteorological observations has never been digitized because the data have either thus far not been considered valuable or just not been known about. However, even in the rather recent past, the availability of satellite data records (and the computer codes needed to read and process them) is an issue that needs to be addressed  (Poli et al.
CLIMATE REANALYSES. Reanalyses are generated by combining first-guess estimates of the state of the atmosphere, ocean cryosphere, land, and so forth, usually defined by model forecasts, with data from a range of observing platforms (surface, upper air, satellites) using objective methods named data assimilation systems. The availability and quality of observations affects the confidence that can be placed upon reanalysis datasets (e. g. ,  Compo et al. 2011; Hersbach et al. 2017; Uppala et al. 2005). Some reanalyses provide multidecadal or longer time series of gridded variables. However, they can only be used to monitor the evolution of the Earth system climate if they are not sensitive to changes of the observing system. Given that the observing system has been changing dramatically over the past decades, especially since the 1970s with the start of the satellite era, it is necessary to try to rescue and recover as many past observations as possible, so that the number of observations does not change too strongly throughout the years spanned by a climate reanalysis and so that different observations can be used together to characterize their biases or other uncertainties.
The work presented here was performed within the framework of ERA-CLIM2, a 4-yr research project funded by the European Union Seventh Framework Program (FP7). It aims to produce coupled reanalyses, which are physically consistent datasets describing the evolution of the global atmosphere, ocean, land surface, cryosphere, and carbon cycle [see  Buizza et al. (2018)  for an overview]. The main contribution of the ERA-CLIM2 project to climate science has been to improve the capacity for producing stateof-the-art climate reanalyses that extend back to the early twentieth century. Observational data rescue was an important part of the project, including data rescue of historic in situ weather observations around the world and reprocessing of satellite climate data records. Specifically, the work encompassed 1) data rescue for in situ observations, their quality control, and provision of metadata; 2) satellite data rescue, reprocessing, and intercalibration; and 3) provision of boundary constraints and external forcing.
SURFACE METEOROLOGICAL DATA. Observations of pressure and wind and, sometimes, temperature at the surface are assimilated into reanalyses. Other surface observations, such as those of precipitation, have so far mostly been used in reanalysis to evaluate the effectiveness of the forecast of those variables. It is important to apply rigorous quality control (QC) to these observations and the Hadley Centre Integrated Surface Database (HadISD; version 2. 0. 0; Dunn et al. 2016 ; available from www. metoffice. gov. uk/hadobs/hadisd/) has been further developed under ERA-CLIM2 to improve and extend the QC of a subset of subdaily observations back to 1931 obtained from the National Oceanic and Atmospheric Administration/National Centers for Environmental Information (NOAA/NCEI) Integrated Surface Database  (Smith et al.
Work was also performed on the QC and homogenization of the data (e. g. ,  Bližňák et al. 2015; Hunziker et al. 2017). The data were submitted to the corresponding data centers, such as the International Surface Pressure Databank (ISPD;  Cram et al. 2015)  for pressure and the International Surface Temperature Initiative (ISTI;  Rennie et al. 2014 ) for temperature; additional download links for individual data collections are given below. Additionally, metadata for these stations are included in the ERA-CLIM registry (see below). Table  1  provides a brief listing of important sources. These are discussed in more detail below. Climatológicos, 1937-74. These publications presented data for Luanda from 1937 until 1974 (with a gap in 1969) and for other Angolan stations from 1953. The entire collection was imaged and subsequently recovered by optical character recognition (OCR) techniques; ERA-CLIM2 complemented the work started in ERA-CLIM  (Stickler et al.
The data were imaged, and the 1910-14 period and the 1947-60 period (in addition to the 1915-46 period in Anais das Colonias recovered previously in ERA-CLIM) were digitized. This is a very complete set of data containing the same variables as the Angola set mentioned above, as well as soil temperatures (with depth reaching 1. 5 m below ground level) and grass temperatures. Priority was given to surface/MSL pressure, and for the 1947-60 period all data (recorded every 2 h) have been digitized and sent to ISPD. Moçambique-Boletim Mensal, 1934-56. The other Mozambique dataset was found to contain subdaily data for Maputo only, for 1934-44, and for several stations in the 1951-56 period. There is a gap between 1945 and 1950 in the Instituto Dom Luiz collection of these publications. Besides imaging the whole set, it was chosen to digitize the 1951-56 period for the following stations: Beira, Inhambane, Quelimane, Mossuril, Murrebué, Tete, Vila Cabral, and Lumbo.
Quality controlled Archive; see  Durre et al. 2006 ) to the 1940s. The work comprised three parts: • complementing data already existing in electronic format but which were incomplete (missing stations, missing levels, etc. ); • digitizing new sources from handwritten tables; and • integrating computerized "views" of old punch cards in exotic formats (the handwritten tables were manually keyed by punching machines to paper punch cards in the 1970s and 1980s, and in the late 1980s the paper punch cards were copied to nine-track magnetic tapes "as is"). The effort in ERA-CLIM2 related to this source of data included decoding information from obsolete nine-track tape media and compiling a digital dataset. The final dataset was compiled from these three sources. The data were formatted, quality controlled, and delivered to CHUAN, version 2.
1. They were also used for various analyses  (Lavrov and Sterin 2017; Sterin and Lavrov 2017). Central and northern European data, 1900s-30s and polar expeditions. The digitization of several large collections of upper-air data begun in ERA-CLIM was continued in ERA-CLIM2. An important source for early data was the German "daily weather report," , which contains data from many stations in the Atlantic-European sector as well as Russia. Also data from the Netherlands and Finland were digitized (Fig. 1 ). These three data sources provide rich information on the early period of aerological measurements (i. e. , from the 1900s to the 1930s); in total, 200,000 station days were digitized. Also noteworthy are upper-air data from early Arctic expeditions such as Greenland expeditions in 1912/13 (Switzerland), 1926-31 (University of Michigan), and 1930/31 (Germany) or British, American, and Australian Antarctic expeditions.
Asian and African sources, 1920s-50s. A large amount of data were digitized from the reports of the India Meteorological  Department (1928-42)  and the Pakistan daily weather report. A further important source was upper-air data from Egypt (1920-53). These sources clearly leave a large imprint on the station map (Fig. 1 ) reflecting a colonial world at that time  (Brönnimann and Wintzer 2018). As an example of a further Asian source, Fig. 2   2  and Fig. 1 ). Pilot balloon data for the period 1919-57 were digitized from handwritten reports. Major efforts have been undertaken to locate records of upper-air data from France and French ex-colonies in 82 French archives, to image all the located records (one million images generated within ERA-CLIM and ERA-CLIM2), to create new long-term series of upper-air wind, and to compile metadata on these historical observations.
The Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) analysis  (Rayner et al. 2003)  was further developed under ERA-CLIM and ERA-CLIM2, revising both the SST and sea ice components to version 2. Titchner and Rayner (2014)  document the new sea ice analysis (which is available at www. metoffice. gov. uk/hadobs/hadisst2/). The representation of Antarctic sea ice remains fairly crude in this analysis prior to 1973, so a search was undertaken for more observations. This search was continued under ERA-CLIM2 and has yielded information not only on sea ice but also (primarily) on surface meteorological variables for the Southern Ocean for the late nineteenth and early twentieth centuries. These data, mostly from ships' logbooks in the Southern Ocean, have been imaged but not keyed, from the original documents.
Development of HadIOD. The Met Office Hadley Centre Integrated Ocean Database (HadIOD;  Atkinson et al. 2014 ) was first developed under ERA-CLIM and further improved under ERA-CLIM2 to produce version 1. 2. 0. 0. HadIOD is a database of global ocean temperature and salinity observations, combining data from surface-only and profiling instruments, with quality flags, bias adjustments, and uncertainty estimates for each observation (where possible). Chief data sources are the International Comprehensive Ocean-Atmosphere Data Set (ICOADS)  (Woodruff et al. 2011)  for SST observations and the Met Office Hadley Centre EN series dataset  (Good et al. 2013)  for temperature and salinity profile observations. The ultimate aim of HadIOD is to provide input for coupled data assimilation. HadIOD. 1.
2. 0. 0 has undergone the following recent developments: (i) greater time coverage (now starting in 1850); (ii) new data sources, for example, high-temporal-resolution global tropical moored buoy array data; (iii) improvements to bias corrections; (iv) a new output format for assimilation; and (v) improved quality control of data from ships and drifting buoys over their measuring lifetimes. SNOW DATA. An effort to establish a global dataset of distributed snow observations resulted in a new collection of snow water equivalent (SWE) values measured at snow courses of the Northern Hemisphere. SWE recordings from distributed snow courses were obtained covering the regions of Russia (and the former Soviet Union), Canada,   4 ), the total number of observations being around 1. 2 million. The SWE values were calculated from snow depth and density samples.
Depth measurements were performed at up to 100 locations obtained at a constant interval through the snow course. The snowpack bulk density is sampled more sparsely, typically every tenth depth sample. In addition to SWE, the dataset has information on snow course center coordinates, mean snow depth, mean density, and the day of an individual observation. The typical frequency of observation at a single site varies from once a week to once a month, and the observations were carried out throughout the period of seasonal snow cover. Figure  4  shows the locations of included snow courses. In addition to SWE, historical snow depth data from Russia were digitized and processed. Again, the digitization mainly extended the list of stations in the snow parameter dataset. Furthermore, 20 additional stations were digitized that were not available electronically.
Owing to numerous metadata records on historical changes in snow observational practices in the former Soviet Union and on the changes in QC processing and quality flag codes, it became possible to merge the data into a consistent database. (The data are available from www. meteo. ru. ) Figure  5  shows climatologies of snow depth for the months of April, October, and November  (Wegmann et al. 2017). The bottom row shows corresponding maps from the ECMWF twentieth-century reanalysis (ERA-20C;  Poli et al. 2016) , which does not assimilate snow depth data. The figure shows that ERA-20C realistically reproduces the broad features of climatological average snow depth. HISTORICAL SATELLITE DATA. Satellite data are used in almost all global reanalyses covering the last 50-60 years. However, they only became available in the mid-1960s, and the satellites and instruments were mostly built for the purpose of weather monitoring.
Their use in a reanalysis requires a quantification and correction of longterm trends due to changes in the characteristics of satellites and performance of sensors during their operational lifetime in space, as well as systematic differences between instruments of the same kind (e. g. , in their spectral response). A reprocessing of the data that applies corrections for such effects is fundamental to serve the generation of physically consistent data records of geophysical variables by reanalysis. Consequently, in ERA-CLIM2, further improvement of reanalyses of the satellite era was contributed to by producing reprocessed and recalibrated satellite data records and by including more observations from early satellite instruments. An assessment of satellite sounder radiances suitable for use in reanalyses, mainly from the 1970s, was made for ERA-CLIM2 and is documented in Table  3.
Specific efforts addressed four different satellite time series:  radiances and atmospheric motion vectors  (Borde et al. 2014)  for the period 1982-2014. The recalibration of geostationary radiances is a complex process involving multiple steps: 1) collection of geostationary and reference instruments datasets; 2) selection of reference instruments and bringing the reference data to match spectrally with the geostationary measurements, which involves rigorous data analysis and research; 3) finding collocations between the geostationary and reference data (which is computationally expensive); and 4) use of the collocated measurements to determine the recalibration coefficients. The scope of effort needed for satellite data rescue and reprocessing is no less daunting than that for in situ data rescue  (Poli et al.
2017). As part of satellite data rescue activities, advice on the use of historical satellite datasets was provided. Even more important, the capability to simulate the measurements of historical satellite instruments to allow monitoring and assimilation of radiances from such instruments has been enhanced. The Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) model (RTTOV;  Saunders et al. 1999 ) has been updated since ERA-Interim was produced to include more of the historical sensors. In particular biases of individual instruments were explored, and the work has shown that some spectral responses previously assumed in the radiative transfer model needed updating. The new RTTOV-12 model reduces some of the biases due to instrumental effects and also allows more accurate calculations with an increased number of pressure levels through the atmosphere.
For example, the cell pressure of the Stratospheric Sounding Unit (SSU) reduced after launch and new coefficients have been developed to account for this  (Nash and Saunders 2015). Coefficients for early satellite instruments have also been updated assuming the atmospheric CO 2 concentration for an earlier period, rather than using current CO 2 concentrations. DATA MANAGEMENT, DATA USE, AND DATA PRODUCTS. For the digitizing process and management of surface, ship, and upper-air data, a metadatabase was developed within ERA-CLIM and ERA-CLIM2. This database is public (http://eraclim -global-registry. fc. ul. pt/) and will be helpful also to other projects as it contains metadata on thousands of surface and upper-air stations as well as ship logs. In addition to the station names, various station numbers, coordinates, and time period of measurements, the database contains information on the archive, variables covered, the state of the process, or data level (imaged, digitized, formatted, quality controlled, etc.
) and offers the possibility to directly link to the data. This registry will allow others to make full use of the information compiled within ERA-CLIM and ERA-CLIM2 and to share their own data rescue information with the community. In addition to updating existing repositories (see next section), observations rescued and processed in ERA-CLIM and ERA-CLIM2 were also directly used for the generation of reanalyses. Surface pressure observations were assimilated into both ERA-20C and the Coupled ECMWF Reanalysis of the Twentieth Century (CERA-20C;  Laloyaux et al. 2018) , and some of the processed satellite data were assimilated into the fifth major global reanalysis produced by ECMWF (ERA5; Hersbach and Dee 2016). The satellite data are and will become available from EUMET-SAT (under http://navigator.
eumetsat. int). As an example, Fig. 7  shows the case of a typhoon on 18 September 1906 that hit Hong Kong. ERA-20C, which only included one station within the region displayed (Hong Kong), did not reproduce the typhoon. Within ERA-CLIM, data from the South China Sea were digitized (large dots), and they were assimilated into CERA-20C. The CERA-20C ensemble mean produces a low pressure system, albeit too weak. However, ensemble member 9 of CERA-20C shows a stronger cyclone. In ERA-CLIM2, the South China Sea dataset has now been extended substantially, so that future reanalyses will be able to better simulate typhoons over South Asia in the early years. Most of the observations digitized and processed within ERA-CLIM and ERA-CLIM2 were not assimilated into ERA-20C or CERA-20C, but they were used for their evaluation (e.
g. ,  Stickler et al. 2015). In addition, a test reanalysis was performed [European Reanalysis of the presatellite era (ERA-PreSAT);  Hersbach et al. 2017] , covering the period 1939-67 and assimilating the CHUAN, version-1, upper-air data  (Stickler et al. 2010) , demonstrating the potential of using historical upper-air data for reanalyses. Ongoing and future reanalysis efforts will be able to make use of the millions of profiles digitized as well as of the experience in generating ERA-PreSAT. Likewise, snow observations are not directly assimilated but were used to evaluate the reanalyses and their respective land surface products (Fig. 5 ).