dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The objective of this work was to measure the incidence of individual histological subtypes of sarcomas and TIM according to the 2013 WHO classification. These cases were collected from the NETSARC+ database, combining the previous RRePS, ResOs and NetSarc databases. This work, supported by the French NCI, allowed to measure the incidence of sarcomas and TIM in a nationwide level. The mandatory central pathology review, in place since 2010, has facilitated the constitution of a close to exhaustive nationwide collection of patients with sarcoma and TIM. Since 2013, the number of patients included in the database per year is relatively stable suggesting that the database may indeed be close to exhaustiveness. We stopped the description in 2016, since the period spanning from 2017 to 2019 is still being monitored and updated by the CRAs of NETSARC+.
It is important to remember that altogether patients with rare cancers represent 22% of all patients with cancers, and about 30% of the mortality due to cancer  [29]. This study has many limitations. We cannot exclude that patients may not reach our network despite the administrative incentive. This is true in particular for bone sarcoma and TIM (e. g. chondroblastomas, osteoblastoma, aneurysmatic bone cyst, etc. ) which were collected more recently. The work must also adapt to the rapidly evolving classification of sarcomas, including new molecular sub-classifications, which are not described here (for instance GIST, the novel NTRK sarcoma subgroup, BCOR, CIC-DUX4 sarcomas). To further explore the exhaustivity of the NETSARC+ database, an ongoing project connects it to the social security data base (SNDS) (the Health DataHub Deepsarc project), the single payer in France covering all citizens, across all diseases.
This work was supported by  Institut National Du Cancer (FR) NETSARC+, Institut National Du Cancer (FR) RREPS, Institut National Du Cancer (FR) RESOS, Institut National Du Cancer (FR) NETSARC, Institut National Du Cancer (FR) LYriCAN  ( INCa-DGOS-INSERM_12563 ),  Agence Nationale de la Recherche (FR) Institut Convergence PLASCA , ( 17-CONV-0002 ),  Agence Nationale de la Recherche (FR) LabEx DEvweCAN  ( ANR-10-LABX-0061 ),  Agence Nationale de la Recherche (FR)   RHU4   DEPGYN  ( ANR-18-RHUS-0009 )  Institut National Du Cancer (FR) INTERSARC ,  Fondation ARC pour la Recherche sur le Cancer Unicancer, Ligue Contre le Cancer (FR) Unicancer ,  Ligue Contre le Cancer Comitede l'Ain Canopee , and by  EUROPEAN COMMISSION (EU) EURACAN  ( EC 739521 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We shed new light on the linkages between age diversity and technological innovation, and explore the moderating effect of human resource practices on such relationships. Based on a linked dataset that contains cross-sectional survey data and longitudinal employereemployee data from Luxembourg, we show that the effect of age diversity on innovation depends on the age distribution pattern of employees: positive for firms characterized by heterogeneous age groups (variety), negative for those dominated by polarized age groups (polarization). HR practices such as information sharing mitigate the adverse effects of age polarization on innovation. Practices enhancing development such as training are found to play a significant and negative role in moderating the relationship between age diversity and innovation.
The current paper thus integrates two very different streams of research, one that deals with workplace diversity and the other with HR practices. We use cross-sectional data from the Luxembourg employer survey over the period 2010e2012 (one wave) and Luxembourg's longitudinal linked employer-employee data, available annually from 2009 to 2011 (three waves). Combining these two databases creates a sample of 1422 Luxembourg firms, among which we find that distinct HR practices could have different moderating impacts.
Sample For this study, we use a rich data set derived from combining two sources. The first, the Luxembourg Employer Survey (LES), was conducted in 2013 by the Luxembourg Institute of Socio-Economic Research (LISER). This cross-sectional data set refers to the period 2010e2012 and contains information about companies (sector of activity, groups, number of employees, sales, and geographic market), their HR practices, and their technological innovations. It accounts for nonresponse and survey design probabilities and ensures representativeness. Our second data set is the Luxembourg Longitudinal Linked EmployereEmployee Data (LLEED), available from Luxembourg's annual General Inspection of the Social Security report, for the years 2009e2011. This provides registry data for all employees in Luxembourg, including each worker's age, gender, and nationality.
In this paper, the dependent variable and independent variables are computed from two independent databases. The technological innovation is computed from the cross-sectional survey data referring to the period 2010e2012 and the age polarization and variety are computed from the longitudinal administrative dataset of the period 2009e2011 (Appendix 1). Thus, the independent variables (polarization and variety) in 2009 and 2010 are prior to technological innovation of the period 2010e2012, they can be considered internal instruments for endogenizing polarization and variety in 2011. As additional instruments, we include the lagged total number of people who quit and of new employees. To test the potential interplay of age diversity and HR practices, we include interaction terms in the models.
Second, to embrace the benefits of variety, training and development practices should be adapted to each age category. Because younger and older people possess different skills and abilities, failing to suitably adapt development programs to their characteristics will hinder organizational performance and innovation. These results suggest that there is no single applicable theory of corporate age structures, and more research is needed on the moderating or even mediating roles of HR practices, particularly using longitudinal studies. This study is not without limitations. First, we had access to longitudinal data for HR practices but not for technological innovation, which would have helped us to control for reverse causality. Producing databases to take into account this methodological aspect could constitute a first step for further research on the subject.
Acknowledgements This research is part of the "WorkAgeing" project supported by the  Fonds National de la Recherche, Luxembourg  (contract  FNR/ C16/SC/11344520 ) and by core funding from  LISER  from the  Ministry of Higher Education and Research of Luxembourg. The authors are grateful to  Anne-Sophie Genevois ,  Konstantinos Tatsiramos ,  Sam Cosaert  and three anonymous reviewers for their valuable comments on previous versions of the paper.
The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)  or 14 and 35 GHz)  provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.
Dataset Preparation The processing flow is driven by a pre-generated list of the ±15-min time coincidences between the orbital ground trace of CloudSat and TRMM, and CloudSat and GPM, obtained from orbital propagation using daily two-line elements (TLE) ephemeris, providing sufficient accuracy for aligning orbital locations to within several seconds. CloudSat was initially placed into the tightly controlled A-Train sun-synchronous satellite formation (owing to the afternoon 1330 Local Time of Ascending Node, or LTAN). The operation of the CloudSat Profiling Radar (CPR) began in June 2006. Due to satellite power issues in April 2011 which temporarily suspended science data collection, CloudSat was operated in a "daytime only" (CPR operations during the ascending orbit) mode data after mid-2012, with periodic data outages since that time  [10] [11] [12].
The CloudSat-TRMM dataset spans the period between  16  June 2006 (first day of CloudSat data) and 30 September 2014. A total of 19,610 ± 15-min orbit coincidences files are available within this time span. In 2019, the CloudSat Release-5 (R05) data products were released, and all data reprocessed back to the beginning of the mission in June 2006. While the CloudSat-TRMM dataset record covers the complete 2006-2014 overlap period, the data record for several of the R05 CloudSat Level-2 products is currently incomplete after late 2016. Therefore, the current (as of early 2021) CloudSat-GPM dataset spans the period between 18 March 2014 and 30 September 2016. A total of 6502 ± 15-min orbit coincidences files are available within this time span. To enable long-term studies (20+ years), the TRMM and GPM products should be consistent as possible.
With the release of Version 8 TRMM products, the same radar and radiometer algorithms for GPM are run on the TRMM data (using only the Ku-only version of the radar-only and the combined radar-radiometer). Therefore, the content and format of the CloudSat-TRMM and CloudSat-GPM coincidence files are identical. A description of the file content is provided in Appendix A. A schematic of a coincidence segment is shown in Figure  1. For each coincidence, the CloudSat 2B-GEOPROF (W-band radar profiles, uncorrected for attenuation), and the GPM 1C. GPM. GMI (Level 1C are the intercalibrated GMI brightness temperatures, denoted as TB) and 2A. GPM. DPR (Ku-and Ka-band radar profiles, uncorrected for attenuation) files are located. Since the CPR has finer horizontal resolution and along-track sampling (~1.
1-km) than the GPM radars (~4-5 km), the matching procedure between each CloudSat beam and DPR is intentionally a very simple method, based on a nearest-neighbor approach to avoid any spatial or temporal resampling artifacts. The matching procedure locates the (latitude, longitude) coordinates of the Ku-and Ka-band radar beams (NS and MS groups in the 2A. GPM. DPR file, respectively) that are closest to the CPR beam location. With this approach, the same DPR location is reported about four to five times, as the CPR moves across the DPR swath. Remote Sens. 2021,  13,  3 of 34 The CloudSat-TRMM dataset spans the period between  16  June 2006 (first day of CloudSat data) and 30 September 2014. A total of 19,610 ± 15-min orbit coincidences files are available within this time span. In 2019, the CloudSat Release-5 (R05) data products were released, and all data reprocessed back to the beginning of the mission in June 2006.
While the CloudSat-TRMM dataset record covers the complete 2006-2014 overlap period, the data record for several of the R05 CloudSat Level-2 products is currently incomplete after late 2016. Therefore, the current (as of early 2021) CloudSat-GPM dataset spans the period between 18 March 2014 and 30 September 2016. A total of 6502 ±15-min orbit coincidences files are available within this time span. To enable long-term studies (20+ years), the TRMM and GPM products should be consistent as possible. With the release of Version 8 TRMM products, the same radar and radiometer algorithms for GPM are run on the TRMM data (using only the Ku-only version of the radar-only and the combined radar-radiometer). Therefore, the content and format of the CloudSat-TRMM and CloudSat-GPM coincidence files are identical.
No averaging or resampling is carried out, nor any attempt to resolve the lowest-resolution 10-37 GHz channels to a common resolution. The final step involves bringing in additional Level-2 CloudSat and GPM data products, such as the associated ECMWF-AUX (European Center for Medium Range Weather Forecasts (ECMWF) forecast model fields interpolated to each CloudSat radar vertical bin). These environmental variables are useful for forward simulations of radar reflectivity profiles and passive microwave brightness temperatures. Additional data from the MODIS-AUX auxiliary product (thermal channels from the Moderate Imaging Spectroradiometer (MODIS) imager onboard the Aqua satellite, matched to each CloudSat beam), are included for the eleven thermal infrared (IR) or near-IR channels (channels 20 and 27-36).
CloudSat Sampling and Resolution Segments Once the coincident CloudSat and DPR beam positions are located, the next step is to match their vertical locations. For each CPR profile (latitude, longitude) location, the associated (latitude, longitude) coordinates of each GPM sensor (DPR and GMI) are located, that are closest to this CPR location. For the vertical bin matching, the nearest DPR bin is located and matched to each CloudSat vertical bin height (i. e. , nearest neighbor). For the DPR off-nadir positions, the DPR slant path geometry is not considered when matching to CloudSat, except to adjust the DPR elevation by the cos(zenith) term. The 2A. GPM. DPR dataset carries three radar profiles: the 176-level Ku-band normal scan (NS), the Ka-band matched scan (MS), and the 88-level Ka-band high-sensitivity scan (HS).
After 21 May 2018, the DPR transmit timing was reprogrammed and the HS Kaband beams were steered to expand the Ka-band MS swath, providing near-coincident Ku/Ka-band coverage across the full 245-km swath  [13, 14]. While the dataset described in this manuscript does not currently extend in time past September 2016, future versions will incorporate this change. For each 240-m CPR vertical bin, the DPR radar profiles are searched from bottomto-top until the first bin that lies above each CPR bin is identified, and this is reported as the associated DPR-matched bin (Figure  3 ). This procedure is repeated for each of the NS, MS and HS profiles. Since the NS and MS profiles are reported at 125-m bin spacing, with this approach, only every other NS and MS bin is used (HS is reported at a 250-m bin spacing, so all bins are used).
After 21 May 2018, the DPR transmit timing was reprogrammed and the HS Ka-band beams were steered to expand the Ka-band MS swath, providing near-coincident Ku/Ka-band coverage across the full 245-km swath  [13, 14]. While the dataset described in this manuscript does not currently extend in time past September 2016, future versions will incorporate this change. For each 240-m CPR vertical bin, the DPR radar profiles are searched from bottom-totop until the first bin that lies above each CPR bin is identified, and this is reported as the associated DPR-matched bin (Figure  3 ). This procedure is repeated for each of the NS, MS and HS profiles. Since the NS and MS profiles are reported at 125-m bin spacing, with this approach, only every other NS and MS bin is used (HS is reported at a 250-m bin spacing, so all bins are used).
This simple nearest bin in latitude, longitude and elevation does not modify or average any values in the native dataset. However, full-swath segments are also provided for users who desire different bin-matching methods. For all three radar profiles, only the "uncorrected" (zFactorMeasured) DPR reflectivities are carried in the coincidence dataset, to be self-consistent with the nature of the CPR reflectivity profile, and to not carry any assumptions on any particular attenuation-correction technique used to produce precipitation water content profiles. Figure  4  depicts the corresponding radar vertical cross sections, corresponding to the coincidence shown in Figure  2. The horizontal axis extends across the full CloudSat curtain (solid black line in Figure  2 ), which (in this example) covers 1540 CloudSat beam positions.
The top three panels extend to 15-km height above the reference surface, showing the (uncorrected for attenuation) equivalent radar reflectivity profile (dBZ) from Cloud-Sat, DPR Ku-and Ka-band MS (rows 1, 2 and 3, respectively) radar profiles scaled as shown in the right-side color bar. The digital elevation map (DEM) used by the CloudSat and GPM data is shown as a heavy black line near the surface (not obvious here, but it is evident for the over-land cases that are over steep terrain). The fourth row shows the trace of the 13 GMI channels (increasing in frequency from top to bottom). The fifth row shows the surface classification index taken from the GMI Goddard Profiling Algorithm (GPROF)  [15]  along the CloudSat track. In this example, the surface class flips between ocean and coast as CloudSat moves across the island of Java, and finally to a heavy vegetated land background as the orbit moves across the Java Sea onto Indonesia.
The sixth row shows the TB from the eleven MODIS thermal channels (channels 20, and 27-36) copied over from the MODIS-AUX auxiliary ancillary data product. Table  A4  in Appendix A lists these eleven MODIS channel bandwidths. Not shown but also included from MODIS-AUX is the 3 × 5 subset of the 1-km cloud mask, copied from the Aqua MODIS MYD06_L2 data product. One of the important uses of this dataset is to use the CloudSat data to better identify "cloud-free" scenes to screen clouds for the interpretation of land surface characteristics  [16]. To assist, the coincidence dataset carries the cloud mask from the MODIS-AUX and 2B-GEOPROF-LIDAR  [17]  data products. CloudSat is reported at 240-m vertical bin spacing, whereas at nadir DPR is reported at 125-m for the normal scan and matched scan, and 250-m for the high sensitivity scan.
For each of NS, MS and HS, the "bin-matched" DPR bin is reported as the bin whose top lies just above the CloudSat bin top. For all three radar profiles, only the "uncorrected" (zFactorMeasured) DPR reflectivities are carried in the coincidence dataset, to be self-consistent with the nature of the CPR reflectivity profile, and to not carry any assumptions on any particular attenuationcorrection technique used to produce precipitation water content profiles. Figure  4  depicts the corresponding radar vertical cross sections, corresponding to the coincidence shown in Figure  2. The horizontal axis extends across the full CloudSat curtain (solid black line in Figure  2 ), which (in this example) covers 1540 CloudSat beam positions. The top three panels extend to 15-km height above the reference surface, showing the (uncorrected for attenuation) equivalent radar reflectivity profile (dBZ) from CloudSat, DPR Ku-and Ka-band MS (rows 1, 2 and 3, respectively) radar profiles scaled as shown in the right-side color bar.
The digital elevation map (DEM) used by the CloudSat and GPM data is shown as a heavy black line near the surface (not obvious here, but it is evident for the over-land cases that are over steep terrain). The fourth row shows the trace of the 13 GMI channels (increasing in frequency from top to bottom). The fifth row shows the surface classification index taken from the GMI Goddard Profiling Algorithm (GPROF)  [15]  along the CloudSat track. In this example, the surface class flips between ocean and coast as CloudSat moves across the island of Java, and finally to a heavy vegetated land background as the orbit moves across the Java Sea onto Indonesia. The sixth row shows the TB from the eleven MODIS thermal channels (channels 20, and 27-36) copied over from the MODIS-AUX auxiliary ancillary data product.
Full-Swath Segments As mentioned above, to provide the user more flexibility in remapping or matching these two datasets, "full swath" GMI and DPR segments are added to each dataset, which cover an additional 50 scans on either side of the coincidence segment (provided in the SWATH sub-group, Appendix A). For example, 1C. GPM. GMI files typically contain about 2930 scan lines (one full orbit) and 221 pixel locations each. If the CPR coincidence locations were located between GMI scan lines 1000-1050 (51 scans, denoted by "GMI-1" and "GMI-2" in Figure  1 ), then the "swath" segment carries the full swath of GMI TB data between scan lines 950 and 1100 (e. g. , an array sized 151 × 221 × 13, along with the associated latitude and longitude arrays). A similar procedure is carried out for each of the DPR NS, MS, and HS radars.
During the TRMM era, this alignment happened frequently with NOAA-18. For each of the CloudSat-TRMM coincidences, whenever NOAA-18 flies within  15  As is seen in Section 3, these passive MW sounder data fill in gaps in the coverage of cold-season precipitation, especially at higher latitudes where coincidences with CloudSat are more frequent. In particular, the CloudSat-GPM coincidence dataset has proven useful for the assessment of the consistency of precipitation produced from the "high-frequency" channels of GMI (the S2 channels at 166 and 183 GHz) and similar channels on ATMS and MHS. An example of the collocated GMI, MHS and ATMS measurements of a precipitation event over Greenland on 13 September 2014 is shown in Figure  5. Even though MHS and ATMS are across-track scanning sounders with coarser resolutions than GMI, the correspondence between each is obvious.
Applications to Cold-Season Precipitation With their frequent coverage over high-latitudes and wide scan swath, high-frequency (e. g. , frequencies above ~90 GHz) passive MW sensor-based precipitation data have significant potential to provide estimates where cold-season precipitation (i. e. , precipitation reaching the surface in its solid phase, and also drizzle and sleet) is an important contribution to total annual precipitation  [19] [20] [21] [22] [23]. In this section, several recent investigations are highlighted whereby the CloudSat-GPM coincidence dataset is used to validate.
Applications to Cold-Season Precipitation With their frequent coverage over high-latitudes and wide scan swath, high-frequency (e. g. , frequencies above ~90 GHz) passive MW sensor-based precipitation data have significant potential to provide estimates where cold-season precipitation (i. e. , precipitation reaching the surface in its solid phase, and also drizzle and sleet) is an important contribu-tion to total annual precipitation  [19] [20] [21] [22] [23]. In this section, several recent investigations are highlighted whereby the CloudSat-GPM coincidence dataset is used to validate passive MW-based precipitation estimates, as well as to exploit the three-frequency radar data to address limitations of individual radar retrievals of cold-season precipitation  [24].
TB Signatures and Retrieval of High-Latitude Snowfall over Open Oceans and Sea Ice Even though the DPR has higher space-time coverage than the CPR, evidence suggests that it is not sufficiently sensitive to capture the light snowfall events  [25, 26]. The high sensitivity of the CPR to low concentrations of snow and ice particles makes it perhaps the most accurate spaceborne instrument for the retrieval of high-latitude precipitation, especially the shallow cumuliform snowfall  [27, 28] , which is the dominant precipitation regime over the polar regions. Therefore, coincidences of CPR (2006-present) and GMI provide a unique opportunity to study microwave signatures of light oceanic snowfall in high latitudes over open oceans and sea ice beyond the sensitivity of the DPR data. These coincidences can be used not only to understand how the snow cover changes the snowfall scattering over sea ice, but also to construct databases for Bayesian retrieval of light snowfall  [29] [30] [31]  and reduce the uncertainty that might exist in passive retrievals in official GPM products  [15]  that solely rely on DPR-GMI coincidences.