dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey of funders' data-sharing policies Eligibility criteria and search strategy to identify funders. We included funders of clinical research with at least one RCT funded (regardless the design, the population, the intervention or the outcomes) in the course of the years 2016 to 2018, and with an accessible website in English. We searched for non-commercial and commercial funders. Non-commercial funders were retrieved on the Sherpa/Juliet Initiative website  [11]. SHERPA/Juliet is a searchable database providing information on non-commercial funders' policies, especially concerning open access. Commercial funders were selected from different lists of pharmaceutical industry associations: the European Federation of Pharmaceutical Industries and Associations (EFPIA)  [12] , the Pharmaceutical Research and Manufacturers of America (PhRMA)  [13] , the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA)  [14]  and "Les Entreprises du Medicament" (LEEM, a professional organization of pharmaceutical companies operating in France)  [15].
), type of data request review panel (independent or not, or mixing independent members and funder members), data request methods (through data-sharing platform, by contacting trial investigators), specific funding for data-sharing, restriction of duration of data availability time frame for sharing data. We classified policies as being "encouraging policies" (policies that mention data-sharing without a strict requirement) or "mandatory policies" (policies that require the implementation of the recommendations and/or that mention sanctions in case of non-compliance). Lastly, the following features for each funder were extracted: country, World Bank income category  [17] , and whether it is a signatory of the Declaration on Research Assessment (DORA).
RCTs were identified from the Clinicaltrial. gov website. We used the following filters to search for studies to include: the study starting date (after 1 st January 2019), study type (interventional studies), funder type (industry filter for commercial sample, NIH and "all other" (a filter that identifies universities, charities and other funders) and filter for the non-commercial sample). Among the RCTs found, we excluded studies where funders were not in our list of funders with data-sharing policies (a study was eligible if it was funded by at least one funder with a data-sharing policy identified in the first survey), and non-randomised studies. Then we selected two random samples of RCTs: 1/ one for non-commercial funders and 2/ one for commercial funders. RCT data extraction. A data extraction sheet was developed.
For each study included two authors (JG, MS) independently extracted the information entered in the "Individual Participant Data (IPD) Sharing Statement" field on the clinicaltrials. gov website. Disagreements were resolved by consensus and a third author (FN) was consulted in case of persistent disagreement. Outcomes describing RCT data-sharing statements. The primary outcome for this second survey was the intention to share individual participant data in the data-sharing statements of eligible RCTs. The secondary outcomes were the features of the data-sharing statements: data-sharing plan in the registration, information about supporting material availability, information about the protocol and/or statistical analysis plan and/or clinical study report availability, data access methods, restrictions to data access, existence of a specific aim for data reuse, time frame for data availability, free accessibility of data.
Changes to the initial protocol As stated above, we modified the methodology for assessing the compliance of RCTs with the sharing policies of their funders and focused on registered data-sharing plans in clinicaltrials. gov. This was done in accordance with the recommendations of ICMJE which states that "clinical trials that begin enrolling participants on or after 1 January 2019 must include a data-sharing plan in the trial registration". We also made additional minor changes. When information was not publicly available on funder websites, we did not contact them to confirm absence or presence of a policy. Indeed, we previously carried out a similar study on French funders  [18]  in which the information on data-sharing policies was collected via a questionnaire with email reminders and/or calls in case of non-response.
5%) from upper middle-income countries and two (2. 5%) were world organizations. Fifty-three funders (68%) were from Europe and central Asia, eighteen (23%) from North America, five (6%) were from east Asia and Pacific countries. The most widely represented countries were the UK (31 funders), the USA (9 funders) and Canada (9 funders). Twenty non-commercial funders (26%) were DORA signatories. Thirty (out of 78; 38%) non-commercial funders had a data-sharing policy. Table  1  details the characteristics and policies of these funders. Eleven (37%) of the non-commercial funders with a data-sharing policy provided a starting date for their policies. Sixteen (53%) funders asked grant recipients to share data through data-sharing platforms or repositories, one specified the name of the platform (Clinical study data request) and another funder suggested several repositories like Dryad, Dataverse, Figshare and Zenedoo.
io/ujbf2/). Commercial funders. Seventeen (out of 100, 17%) commercial funders included were generic pharmaceutical companies (generic pharmaceutical companies included were those that met our eligibility criteria, and therefore funded at least one clinical trial in the years 2016 to 2018). Ninety were from high-income countries, seven were from lower-middle income countries and three were from upper-middle income countries. The most widely represented countries were the USA (25 funders), Japan (16 funders) and France (13 funders). Forty-four were from Europe and central Asia, twenty-seven were from North America, twenty-two were from east Asia and Pacific countries and seven were from south Asia. None of the commercial funders were DORA signatories. Forty-one (out of 100, 41%) commercial funders had a data-sharing policy that mentioned their commitment to make clinical trial data available on request (none of them was a generic pharmaceutical company).
None of the funders mentioned incentives or rewards for sharingdata, sanctions for non-compliance with the policy or funding for data-sharing procedures in their policies. All commercial funder policies found supported data-sharing. Qualitatively, the distinction between "mandatory" and "encouraging" policies was not applicable because these policies did not apply to an external sponsor but to the commercial funder directly. The policies tended to contain statements supporting trial data-sharing allowing external researchers to request trial data. The full data extracted for all funders is available on OSF (https://osf. io/ujbf2/).
Survey of RCTs funded by funders with a data-sharing policy RCT selection and data extraction. Searches and extraction of eligible RCTs started on 27 th September 2019 and ended with a consensus on 8 th November 2019. One hundred and seventy-one study registrations on Clinicaltrial. gov were found for seventeen different noncommercial funders with data-sharing policies and six hundred and fifty-seven study Non-commercial funders. The hundred trials randomly selected were funded by fifteen non-commercial funders. The most widely represented funders were NIH (61 trials) and Wellcome Trust (8 trials). A data-sharing statement was present for seventy-seven (77%, 95% IC [67-84%]) registered RCTs funded by non-commercial funders. Among the hundred registrations, 12% [7%-20%] had a "Yes" statement to IPD sharing and 12% [7%-20%] an "Undecided" statement.
gov, 77% and 81% respectively for non-commercial and commercial funders detailed a data-sharing plan, but 12% and 59% respectively expressed an explicit intention to share data. This result is in line with another important aspect of transparency, which is the observation that, despite being far from optimal, commercial funders perform better than non-commercial funders in ensuring availability of individual study results on registers such as clinicaltrials. gov [19, 20]. In addition, the low percentages observed suggest difficulties in implementation of funders' data-sharing policies. These difficulties could result from a lack of understanding the policies  [21]  or from reluctance in the part of investigators  [22]. Planning upstream data-sharing and implementing it after a trial can be challenging.
As our survey points out, funders do not provide for incentives for data-sharing, and funding specially dedicated to data-sharing in not put in place by all of them. This lack of incentives and funding could hinder the implementation of the policies put in place. It is also possible that trialists registering their trial on clinicaltrials. gov do not attach importance to data-sharing plans at the time of registration. Importantly, the registration of a sharing plan (even if the plan was not to share data) was not 100% despite the fact that it has been made mandatory by the ICMJE for publishing an article in its member and affiliated journals. In addition, datasharing plans were often unclear and some information was contradictory: in some registrations, it was indicated that there was "no plan to share IPD" while details were given about the procedure to access IPD.
depending on the investigated material). This amorphization effect renders the data collected after 20 h unutilizable. A closer observation of the diffraction peak between open-circuit voltage (OCV) and 20 h (after intensity normalization at the (116) Additional short-range information was obtained based on XAS, which is not affected by the loss of crystallinity of the material during reaction. A total of 70 in situ Cr K-edge XAS spectra were collected during the first lithiation, as shown in Figure  7a  along with the corresponding galvanostatic profile. The different current rates in the electrochemical test from C/8 to C were imposed to complete the discharge within the available beamtime. For the same reason, it was impossible to collect in situ data during the subsequent delithiation, and only selected ex situ XAS spectra were collected at different states of charge.
Considering the difficulty of accurately analyzing each spectrum separately, a chemometric approach implying principal component analysis (PCA) and multivariate curve resolution-alternating least squares (MCR-ALS) analyses was conducted to extract the maximum useful information from this large dataset. A detailed description of this analytical approach is given by Fehse et al. [64]  The PCA variance plot shown in Figure  SI-4 , Supporting Information, suggests that at least three components are needed to describe the whole dataset. These three pure components were reconstructed thanks to the MCR-ALS algorithm (Figure  7c ). The evolution of the reconstructed components during the discharge is shown in Figure  8. The three components, representing the pristine material (Comp1), an intermediate species (Comp2), and the final discharge product (Comp3), were analyzed using standard methods.
Electrochemical measurement began initially at a C/8 rate, but later sped up to C/5, C/2, and then C to complete the collection of the XAS data of the whole lithiation process within the allocated beamtime. A few selected ex situ XAS spectra were measured to study the following delithiation. The energy calibration and normalization of the operando XAS spectra were performed using the Athena software. [71] The normalized operando spectra were analyzed globally using a chemometric approach implying PCA and MCR-ALS, via a procedure described in detail by Fehse et al. [64]  This analysis allows one to extract the pure spectral components that are necessary to describe, via adapted linear combinations, the entire dataset. Such components, as well as the ex situ spectra, were finally analyzed using the Arthemis software.
[71] Fourier transforms of the EXAFS data were performed in the 3-10. 5 Å À1 k range, and the fitting was done in the R-space up to 3. 5 Å. DFT Simulation: Spin-polarized calculations were performed with projector-augmented-wave (PAW) potentials, as implemented in the Vienna ab-initio simulation package (VASP). [72] The exchange-correlation parameterization as provided by PBE was adopted. To correctly include the coulombic interactions among the d levels in the transition metals, we used a Hubbard U ¼ 3. 5 eV for Cr, as successfully used in prior research by Jain et al. [73]  A plane-wave energy cutoff of 600 eV was set. The Vaspkit was used to generate Γ-centered Monkhorst-Pack k-meshes with a recommended value of 0. 04 Â 2π Å À1 , which yields, for example, a k-mesh of 5 Â 5 Â 1 (7 Â 7 Â 3) for the simulated Cr 2 (NCN) 3 (Li 2 NCN) unit cell.
Acknowledgements J. J. A. and K. C. contributed equally to this work and should be both considered as first authors. J. J. A. acknowledges  ALISTORE-ERI  for funding her Ph. D. grant. K. C. is grateful for the financial support from the  Alexander von Humboldt Foundation. The simulation work was supported by the  IT center of RWTH Aachen University  under grant  JARA-HPC (JARA0179 ). Synchrotron Soleil (France)  is acknowledged for providing beamtime at the beamline ROCK. S. Belin  and  V. Briois  are gratefully acknowledged for their expert advice on beamline operation. This work was supported by two public grants overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (Projects  ANR-10-EQPX-45  ( Equipex ROCK ) and  ANR-10-LABX-76-01  ( Labex STOREX) ).
Temperature observations of the upper-air atmosphere are now available for more than 40 years from both ground-and satellite-based observing systems. Recent years have seen substantial improvements in reducing long-standing discrepancies among datasets through major reprocessing efforts. The advent of radio occultation (RO) observations in 2001 has led to further improvements in vertically resolved temperature measurements, enabling a detailed analysis of upper-troposphere/lower-stratosphere trends. This paper presents the current state of atmospheric temperature trends from the latest available observational records. We analyze observations from merged operational satellite measurements, radiosondes, lidars, and RO, spanning a vertical range from the lower troposphere to the upper stratosphere.
A number of previous studies have found that most observational datasets appear to show weaker tropical tropospheric amplification for decadal-scale trends, while this remains a robust feature across several generations of model simulations  (Santer et al. 2005 (Santer et al. , 2008;; Po-Chedley and Fu 2012a; Santer et al. 2017b ). While various homogenization efforts and reprocessing activities have generally reduced the observation-model differences, the trend amplification estimates from radiosondes and most reprocessed microwave sounding satellite products generally remain smaller than those from climate models and moist adiabatic lapse rate considerations  (Thorne et al. 2011; Mitchell et al. 2013). Factors to consider when interpreting model-observation differences include possible errors in climate model forcings  (Solomon et al.
2011 (Solomon et al. , 2012;; Mitchell et al. 2013; Sherwood and Nishant 2015; Santer et al. 2017a) , differences between simulated and observed sea surface temperature (SST) trend patterns  (Mitchell et al. 2013; Kamae et al. 2015; Tuel 2019) , internal variability  (Suárez-Gutiérrez et al. 2017; Kamae et al. 2015; Santer et al. 2019) , and, for satellite retrievals, effects from broad vertical weighting functions  (Santer et al. 2017b). While the vertical profile of temperature trends is an important fingerprint of climate change (e. g. ,  Santer et al. 2013) , the magnitude and vertical structure of trends is often dependent on details of the datasets and homogenization details  (Hartmann et al. 2013) , ''limiting the ability to draw robust and consistent inferences about the true long-term trends.
2017; Zou et al. 2018). Several merged satellite-based datasets have been constructed for providing continuous climate records in the stratosphere from 1979 to the present  (McLandress et al. 2015; Zou and Qian 2016; Randel et al. 2016). Revisiting and reprocessing of stratospheric observations  (Zou et al. 2014; Nash and Saunders 2015)  led to improved consistency of the revised data versions; however, some differences remain  (Seidel et al. 2016). Stratospheric temperature trends from the reprocessed observations and from new models of the SPARC Chemistry Climate Model Initiative (CCMI) showed substantial improvement in the agreement between modeled and observed trends, mainly due to updates of the satellite observations. The range of simulated trends was similar to that in the previous generation of models  (Maycock et al.
g. ,  Anthes 2011; Steiner et al. 2011 Steiner et al. , 2020;; Ho et al. 2017 Ho et al. , 2020) )  and have been identified as a key component for the GCOS (GCOS 2011). These long-term stable observations provide profile information with high vertical resolution in the upper troposphere and lower stratosphere and are well suited for climate studies  (Lackner et al. 2011; Steiner et al. 2009 Steiner et al. , 2011)). We have deliberately chosen not to include reanalysis datasets in this study. While we acknowledge the high value of these products, we chose to compare observational records that are as independent of each other as possible. Since reanalyses strive to assimilate all available data sources, reanalysis products depend on all those datasets and details of the assimilation systems determine whether a reanalysis draws to one dataset more than to another.
Several state-of-the-art reanalyses are currently available or in production  (Fujiwara et al. 2017; Hersbach et al. 2020; Simmons et al. 2020). In this study, we present the latest observational estimates of tropospheric and stratospheric temperature trends based on updated climate records, including novel GNSS RO satellite observations. These estimates include information from gridded radiosonde records and from lidar instruments. We provide an overview of presently available atmospheric observations and recent advances in their development, as well as some of the limitations of the datasets. We discuss variability and trends in both layer-averaged temperatures and vertically resolved data, as well as the associated uncertainties in these results. We also examine the representation of tropical tropospheric amplification in the observations.
The MSU data record has been extended to the present by using the AMSU-A channels that most closely match the MSU channels from 1979 to the present based on satellites from NOAA TIROS-N through NOAA-19, the National Aeronautics and Space Administration (NASA) Aqua satellite, and the European Meteorological Operational (MetOp) satellite series. For this study we use MSU-AMSU-A records from three groups: Remote Sensing Systems (RSS, Santa Rosa, California), the Center for Satellite Applications and Research (STAR) of NOAA/National Environmental Satellite, Data, and Information Service (NESDIS, College Park, Maryland), and the University of Alabama (UAH, Huntsville, Alabama). We use the latest versions of MSU-AMSU-A climate data products, which include RSS, version 4. 0  (RSS 2019; Mears and Wentz 2009a ,b, 2016 , 2017) ; STAR, version 4.
2004; Fu and Johanson 2005; Johanson and Fu 2006; Po-Chedley et al. 2015). We computed TMTcorr by a linear combination of TMT and TLS after  Johanson and Fu (2006). Similarly, we computed a TTS corrected temperature (TTScorr) by a linear combination of TTS and TLS: 1. 18 3 TTS 2 0. 18 3 TLS. Additionally, RSS and UAH provide a product for the lower troposphere (TLT) from a weighted average of measurements made at different incidence angles (RSS) or a weighted combination of TMT, TTS, and TLS observations (UAH) to extrapolate MSU channel 2 and AMSU-A channel 5 lower into the lower troposphere, with a peak contribution near 2 km  (Mears and Wentz 2017; Spencer et al. 2017). AMSU-only stratospheric temperature datasets are available from mid-1998 to present from single channels  (Wang and Zou 2014).
2) STRATOSPHERIC SOUNDING UNIT AND MERGED DATASETS The SSU is a nadir-sounding instrument that flew on NOAA operational satellites from November 1978 to April 2006. The sensor measured the thermal emission of atmospheric carbon dioxide (CO 2 ) in the infrared absorption line near 15 mm. The measurement made use of the pressure modulation technique by putting a cell of CO 2 gas in the instrument's optical path. By modulating the gas pressure of the CO 2 cell, the single CO 2 absorption line was split into three channels with their weighting functions peaking at 30 km for channel 1 (SSU1), 35 km for channel 2 (SSU2), and 45 km for channel 3 (SSU3), respectively. Accordingly, the main contributions for layer-average temperatures stem from heights between 20 and 40, between 25 and 45, and between 35 and 55 km, respectively, spanning the whole stratosphere as illustrated in Fig.
NOAA/STAR provides version STAR SSU v2. 0 (NOAA STAR 2019) as monthly means on a 2. 58 3 2. 58 latitude and longitude grid  (Wang et al. 2012; Zou et al. 2014). The Met Office (Exeter, United Kingdom) provides version UKMO SSU v2. 0 only as 6-month-average global means  (Nash and Saunders 2015) , so that an analysis of latitudinal and seasonal variability is not possible for the UKMO dataset. Comparison of these independently derived versions of SSU show global-mean temperature differences of about 0. 5 K, especially from 1979 to 1990 for the upper channels SSU2 and SSU3. A consistency check of SSU data is taking the average of the lowermost channel SSU1 and the uppermost channel SSU3 and subtracting the middle channel SSU2, which should be close to zero. This difference was found to be within 0.
2 K for NOAA/STAR data but much larger for UKMO data (see Fig. 7  in  Seidel et al. 2016). We therefore use the STAR SSU v2. 0 dataset in the current study. Several merged data records have been constructed by extending the SSU data, which ended in April 2006, with satellite-based datasets from nadir or limb sounders. These data have higher vertical resolution and are integrated vertically with SSU weighting functions to provide SSUequivalent data that are combined with SSU. McLandress et al. (2015)  merged SSU and AMSU data by bridging them with Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measurements  (Fischer et al. 2008). That record is only available until 2012. Randel et al. (2016)  have provided a merged SSU record by combining SSU with Microwave Limb Sounder (MLS) data on the Aura satellite.
MLS measures microwave emission from O 2 and delivers temperatures at 10-90-km altitude with a vertical resolution of about 4-7 km over 20-50 km  (Schwartz et al. 2008). Randel et al. (2016)  also combined SSU with data from the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instrument based on CO 2 emissions, with temperatures retrieved over 16-100 km and a vertical resolution of 2 km  (Remsberg et al. 2008). Zou and Qian (2016)  have derived a merged dataset, STAR SSU-AMSU v3. 0, by combining SSU and AMSU measurements with a variational approach for optimally merging the data. In this work, we use those merged SSU records that have been updated to present, that is, STAR SSU-AMSU v3. 0 (NOAA STAR 2019; Zou and Qian 2016) and SSU-MLS  (Randel et al. 2016). In addition to MLS, SABER, and MIPAS, there are other limb-viewing satellite instruments that provide temperature observations with relatively high vertical resolution (2-4 km).