dataesr/scientific-paragraphs-categorization huggingface dataset:
In the past, for DTM2009 and DTM2013, density data were adjusted to the High Accuracy Satellite Drag Model (HASDM;  Storz et al. , 2005) , via multiplicative scale factors (e. g. , 1. 25 for GOCE;  Bruinsma et al. , 2014)  for consistency, compatibility, and comparability. HASDM is an operational model that is corrected in near realtime through data assimilation. The CHAMP  (Reigber et al. , 1996)  data were also processed using high-fidelity satellite shape and aerodynamic coefficient models by TU Delft  (Doornbos, 2011). The scale factor to best fit the HASDM densities along the CHAMP orbit is 1. 23, which was applied in the construction of DTM2009 and DTM2013. The absolute densities predicted with a model are linked to the aerodynamic coefficients used in the derivation of atmospheric density.
Swarm A The ESA Swarm mission  (Friis-Christensen et al. , 2008) , launched in November 2013, consists of three identical satellites (A and C side-by-side, and B in the higher orbit) in near-polar orbits at about 460 and 510 km initially. They are equipped with accelerometers, but in this study, the GPS-derived densities  (Van den Ijssel et al. , 2020)  are used because of the serious problems and data gaps in the accelerometer data  (Siemes et al. , 2016). The spatial resolution and precision depend on the level of atmospheric drag experienced in orbit, principally on altitude and solar activity. Only the density data of Swarm A (http://swarm-diss. eo. esa. int), one of the lower flying satellites, for high to medium solar activity (a cut-off of F10. 7 less than 100 sfu was applied) was used in DTM2020; the spatial resolution of the selected data is estimated to be approximately 4000-8000 km along the orbit, i.
GRACE Neutral densities inferred from accelerometer measurements on GRACE  (Tapley et al. , 2004)  were computed using the methodology described in  Bruinsma et al. (2004). Compared with the TU Delft processing, the satellite shape was a simple S. Bruinsma and C. Boniface: J. Space Weather Space Clim. 2021, 11, 47  macro model, and the analytical Sentman aerodynamic coefficient model  (Sentman, 1961)  was employed. The GRACE data cover the period 08/2002 through 12/2016 but starting in 2011, 2-3 gaps of 4 weeks each per year occurred, and data quality is slightly worse due to battery issues on the spacecraft. To make the GRACE data more consistent with the more accurate TU Delft densities, scaling factors were determined through comparison, after normalization to the mean altitude, with CHAMP and Swarm A densities when the orbital planes were nearly co-planar (less than 1-hour local time difference).
TLE daily-mean global densities The daily-mean global average mass densities at 250 and 325 km altitude derived from Two-Line orbital Element (TLE) sets covering the years 1967-2013  (Emmert, 2015)  were used or partly used (from 2000 to 2013) in the case of the research model. They are at the low end of the ten altitude levels (250-575 km) on which the TLE densities were computed; the higher altitude data were not used because precision decreases and noise grows rapidly. Although data is provided with daily cadence, the temporal resolution is less because of the 3-day cubic spline smoothing involved in the density retrieval procedure  (Emmert, 2009). Even if the noise is much larger than that in GOCE data at 250 km, the TLE dataset is essential because it covers four solar cycles. Because the scale of the TLE data is not consistent with the accelerometer-inferred and Swarm A densities, this dataset is used in the adjustment procedure to estimate relative density variations and not the absolute value.
SET HASDM density database Space Environment Technologies (SET) has made 20 years of densities available through the SET HASDM Database  (Tobiska et al. , 2021) , which is derived from the US Space Force High Accuracy Satellite Drag Model  (Storz et al. , 2005). These densities are not observations but the output of a model corrected in near real-time through data assimilation. Densities are given every 3 h from 2000 to 2019 on [10°latitude Â 1 h local time] grids, and from 175 to 825 km altitude in steps of 25 km. The SET HASDM data on the altitudes 250, 400, 475, and 800 km are only used for model validation for the entire 20-year time span. The scale of the SET HASDM data (and the data in the next section) is not consistent with the accelerometer-inferred and Swarm A densities. A model bias concerning this dataset is, therefore, to be expected.
Sequential modeling procedure The DTM2020 models were constructed in a sequential procedure because Hp60 was unavailable before 1995, i. e. , not for the entire density database (Table  1 ). In a preliminary step of the procedure, using DTM2013 as a background model, the temperature and partial densities were re-estimated via leastsquares adjustment to better fit the approximately 20-25% smaller observed densities than those used in DTM2013. That unpublished model, DTM2019, was the prior model used in the sequential development depicted in Figure  3. The first model developed was DTM2020_Oper, which is driven by F10. 7 and Kp, and therefore all density data from the sixties to the present-day can be assimilated. Then in a second step, using DTM2020_Oper as background, DTM2020_Intermediate was constructed with F30 and Kp.
Results All models are evaluated by computing the mean and standard deviation (StD) of the density ratios, defined as "observed-over-calculated" or "O/C", in natural log space (ln O/C)). The metrics are described in  (Bruinsma et al. , 2018). The mean of the density ratios represents the bias of a model with respect to the dataset used in the assessment. The model bias thus defined is not an entirely objective quality indicator, and one must consider whether the dataset was ingested into the thermosphere model, and if that was done in an absolute or relative manner. Also, the processing standards applied in the production of the dataset, in particular, the satellite shape and aerodynamic coefficient, because the absolute values of the densities depend on them. The standard deviation of the density ratios, computed as a percentage of the observation, represents a combination of the ability of the model to reproduce observed density variations and (geophysical and instrumental) noise in the observations.
The signal-to-noise ratio of the density data is inversely proportional to the density level, i. e. , it increases with altitude. In the following sections, the relative precisions of the models are evaluated by comparing to the complete datasets in terms of the mean and StD of the density ratios computed with all observations of a dataset, on a yearly and daily basis. The yearly statistics are computed by comparing to all data per calendar year, i. e. , computing one density ratio per year. The StD is then computed with the yearly density ratios in log space. The daily statistics are obtained similarly. More specific information on errors can be obtained by binning the density ratios in latitude, local time, 81-day mean F10. 7/F30, and Kp. Analysis of the mean and StD of the ratios may give clues about specific modeling errors or data inconsistencies.
To eliminate obvious outliers, density ratios are rejected in all assessments if they do not lie in the interval from 0. 4 to 2. 5, and 0. 333 to 3. 0 for TLE data. The density datasets may be inconsistent due to errors and approximations in the satellite geometry models, the aerodynamic force (drag and lift) coefficients, and the surface-to-mass ratios used in their derivation. This will cause the model to be biased concerning those datasets. This inconsistency will diminish when a more physically accurate calculation for the drag force is developed and applied consistently, and all datasets reprocessed using such a standard. However, it is a vast topic for which no consensus is reached, and it cannot be addressed in this paper.
Model precision: overall In this first overall assessment, the relative precisions of the models are calculated by comparing the full datasets in terms of mean and StD of the density ratios computed with all observations of the datasets in Table  1. The results, listed in Table  2 , are also given for the intermediate DTM2020 model constructed with F30 and Kp to quantify the better performance of DTM2020_Res compared to DTM2013 before 2000. Most of the DTM2020 intermediate and research model coefficients are identical, particularly those related to solar activity, and, therefore, the performance under pre-year 2000 conditions is nearly identical. The table is divided into two parts: the top part shows results of comparisons with ingested datasets, while the bottom part presents comparisons to datasets that were not used to construct the DTM models.
As explained in Section 2, in DTM2020, the CHAMP, GOCE, and Swarm A density datasets are presently ingested without scaling factors. The results in Table 2 are obtained using smaller densities, and the 0. 2-0. 3 bias in the MSIS00, JB2008, and DTM2013 density ratios attests to this. DTM2020 fits the data well, with mean density ratios very close to unity. The StD of the density ratios is the smallest (in bold) for the DTM2020 models. The biggest improvements are seen in comparisons with GOCE and Swarm A data, and the former densities at an approximate altitude of 250 km are now reproduced with less than 10% StD. The TLE densities, on the contrary, fit NRLMSISE-00, JB2008, and DTM2013 well on average, whereas the DTM2020 models, as expected, underestimate them by 20-35%. The JB2008 and DTM2020_Res models cannot be compared to the complete TLE datasets because the drivers are not available that far back in the past, and therefore, the comparison was made from 2000 to 2013.
The best results are obtained with NRLMSISE-00, which was fitted to those datasets. Note that the biases of NRLMSIS 2. 0, from 10-26% (for AE-C MESA), are in rather good agreement with DTM2020. Starlette densities at 800 km altitude are wellmodeled with DTM2020 and best with NRLMSISE-00; the StD of JB2008 is high, 39. 3%, and this is due to the absence of predicting a winter Helium bulge  (Keating & Prior, 1968 ). The StD with Stella data, also at about 800 km, understandably is high for the same reason. The HASDM data from 2000 to 2019, selected at four altitudes (250, 400, 475, and 800 km) typically covering GOCE, CHAMP, GRACE, and Stella, is used in the assessment. The data was partly used  (2001) (2002) (2003) (2004) (2005)  in the construction of JB2008, which has small biases. Below 500 km, DTM2020 densities are 17-28% smaller on average, whereas DTM2013 is 5-10% overestimating.
Another salient feature common to all models is a dip, more or less deep, from 2007 to 2009 during the minimum transit from solar cycle 23 to 24. Due to the low solar EUV emissions, which were overestimated with all proxies, and most with F10. 7  (Solomon et al. , 2010). Consequently, all models overestimate density and NRLMSISE-00 most because it uses F10. 7 and is not fitted to data in that period. F30, shown in the bottom left frame of Figure  4 , also overestimated the  EUV flux during the last minimum, but it is smaller than F10. 7. The dip is less obvious in JB2008 because one of its drivers, S10 and its 81-day centered mean S81c, is modified (decreased) for the solar minimum years to better-fit orbit data. A similar scheme seems to have been employed for the minimum years 2017-2019.
But the extremely low values of S81c (less than 55 sfu) cause serious underestimation at 250 km so that applying our always identical thresholding in the comparison procedure led to more than 60% rejection of the 2018-2019 HASDM data at 475 km (also at 400 km; not shown). The JB2008 assessment presented in the DTM2013 paper  (Bruinsma 2015) , using the specific indices files made available, also revealed similar density under-estimation issues for 2010 and 2011. In general, DTM2020 behaves well under all solar activity conditions without having recourse to adjusting the single solar activity driver F30. Below 400 km during solar minima, density is approximately 10% overestimated. This is a consequence of using a proxy for EUV emissions. The DTM2020_Oper model struggles with GRACE and Swarm A and is not as stable over the years as DTM2020_Res or DTM2013 because it uses the F10.
Model precision: geomagnetic storms The assessment under geomagnetic storm conditions is done according to the procedure and on the 13 storms described in  (Bruinsma et al. , 2021) , but with four additional storms at the solar cycle maximum  (31/03/2001; 06/11/2001; 23/05/2002; 07/09/2002). In the procedure, storms are divided into four phases relative to the time of minimum Dst: 12 h preceding onset = phase 1; onset = phase 2; recovery = phase 3; poststorm = phase 4). The models are first de-biased concerning the observations via a scaling factor, then applied to the model densities. The scaling factor is the ratio of the sum of all observations to the sum of all model densities in phase 1. The de-biasing procedure aims to minimize the effect of scaling issues between datasets and non-storm related model errors on the assessment The mean and StD of the density ratios for each phase and the complete interval, as well as the amplitude and timing of the S.
81-day mean solar flux bins The longest dataset, TLE 250 km, is used to bin the density ratios in 81-day mean solar radio flux. The DTM2020_Res model cannot be directly tested due to the absence of Hp60 over the entire interval, but the solar activity-related model coefficients are the same as in the intermediate DTM2020 model. Five mean solar flux bins are defined:  50-100, 100-130, 130-160, 160-190, and 190-290 sfu, and  the results are shown  HASDM densities at 250, 400, and 475 km covering almost two solar cycles have also been used in this assessment. The HASDM 250 km and TLE 250 km results do not fully agree, the small trends from low to high activity in particular, but differences are less than ±5%. The StD, while much larger with TLE 250 km, is largest for low solar activity and reveals a minimum at the medium or medium-high activity for the DTM models, while the StD of NRLMSISE-00 and JB2008 decreases from low to high activity monotonously.
Summary and conclusions Both DTM2020 models have been constructed using the same accelerometer-inferred densities and daily-mean density data after 2000, but DTM2020_Oper is also directly fitted to historical data already assimilated in DTM-2000 (mass spectrometer data in particular;  Bruinsma et al. , 2003) , and the global mean densities at 250 km inferred from TLEs spanning four solar cycles. The CHAMP, GOCE, and Swarm A densities are the most accurate total density datasets thanks to detailed satellite shape models and high fidelity numerical models for the aerodynamic coefficients. They constitute the most accurate absolute density scale, 20-30% smaller than DTM2013 and JB2008 (HASDM) densities. Smaller absolute densities are also predicted with NRLMSIS 2. 0 but through assimilation of data in the mesosphere instead of the accelerometer-inferred densities.
DTM2020 also reproduces the external density datasets more precisely than DTM2013. The EDR density datasets are precisely reproduced with DTM2020_Res, while DTM2020_Oper is significantly less precise but still better than DTM2013. Comparison to HASDM data at 250, 400, and 475 km altitudes confirms that DTM2020_Res is the most precise DTM model. Comparison to HASDM 800 km densities showed that DTM2020_Oper performs better than DTM2020_Res at higher altitude, that both are more accurate than DTM2013 (71 and 34 % more accurate, respectively), and that MSIS is the most stable over the 20 years (Table  3 ). We speculate that this is brought about by more realistic modeling of Helium. Geomagnetic storms are also reproduced much more accurately with DTM2020_Res thanks to the new Hp60 indices than with DTM2020_Oper and DTM2013, which have equal performance (both use Kp).
However, high-resolution density data will remain a scarce commodity in the immediate future, and its provision will remain dependent on missions with other objectives, i. e. , data of opportunity. Such high-resolution data is not available at 800 km at all. Secondly, it is essential that all density datasets available presently are processed according to a common standard to make them consistent. That will facilitate the adjustment and improve the accuracy of the resulting model because the model coefficients can be fitted more precisely.
Acknowledgements. We thank the reviewers for their time and considerable assistance. The  SWAMI  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776287. SB and CB are equally supported by  CNES   APR grant METEOESP. The Hpo indices are distributed by  GFZ  (ftp://ftp. gfz-potsdam. de/pub/home/obs/Hpo) F30 is distributed by  CLS  (ftp://ftpsedr. cls. fr/pub/previsol/solarflux/observation/) The HASDM database is made available by SET (https:// spacewx. com/hasdm/)GOCE and Swarm density data is made available by  ESA  (https://earth. esa. int/eogateway/), or via  TU Delft directly  (http://thermosphere. tudelft. nl) The DTM models and the density uncertainty estimate model are available on Github via the SWAMI website (https://swami-h2020.
The WISP Survey The sample of ELG Pairs was selected from the WISP survey. WISP obtained slitless, near-infrared grism spectroscopy along with direct imaging in the J and H bands, by observing in the pure-parallel mode with the WFC3 on board HST over ∼1000 orbits. 17 The spectra are obtained with the G102 (λ = 0. 8-1. 15 μm, R ∼ 210) and G141 grisms ((λ = 1. 075-1. 70 μm, R ∼ 130), together with direct imaging in the J and JH or H bands (F110W and F140W/F160W, respectively). Out of the 483 fields observed, F140W is used in 190 fields and F160W in 289 fields. 18 For convenience, hereafter the H band is referred to either as the F140W or F160W band, depending on the actual filter used. For some fields, WFC3/UVIS imaging in the F606W and F814W bands are also available. The WFC3 data were reduced through a customized pipeline updated from  Atek et al.
(2010). Each field was examined by at least two reviewers via a customized interface for redshift identification. Each WISP field of view covers a projected area of 2. 3 2. 2 ¢ ´¢ , corresponding to ∼350 kpc 2 at z = 1-2. The WISP HST/WFC3 imaging data have a pixel scale of 0″. 13 pix -1. The spectroscopic redshift was measured from the emission lines detected in the G102 and the G141 grisms. A typical ELG flux limit in WISP for an H α emission line (H αraw , [N II] not corrected) is ∼5 × 10 -17 erg s -1 (5σ). More details regarding the line identification are given in Section 2. 3.
5, are not resolvable with HST's spatial resolution. In this pair sample, the angular separation ranges from 0″. 28-30″. 6, with a median of 10″. 3. Most of our pair sample consists of compact members, while 112 systems (27% ± 3%) are identified as disturbed systems, showing evidence of tidal tails or disturbed morphology based on visual inspections. A total of 413 spectroscopically identified ELG pair systems are selected from a parent sample of 8192 ELGs in 419 WISP fields. The pair sample consists of 24 merging pairs, 108 secure pairs, and 281 wide pairs. For a fraction (108/413, ∼26%) of the WISP pairs, Spitzer Infrared Array Camera (IRAC) observations are also available (PI  : Colbert, ID: 80134, 90230,10041,12093). For these objects we gathered multiwavelength data from HST (UVIS, J, H), ground-based photometry follow-up with the Palomar 5.
1)× over the isolated ELGs, but no more than a marginal SFR increase compared with the compact ELGs (1. 1 ± 0. 3 and 1. 1 ± 0. 1, respectively). We then study the SFR enhancement as a function of pair separations (Figure  5 ). To minimize the redshift effect, we normalize the SFR to z = 0. 75, by adopting the average linear correlations for the pair sample in Figure  4 :   4 , an insignificantly negative slope (-0. 0009 ± 0. 0019) is observed for the full ELG Pairs sample. After binning the data by separations (Figure  5 , red crosses), the marginal increase of SFR toward smaller separation is confirmed, especially from ∼50 h -1 kpc: the SFR is on average 25%-35% higher at 20-5 h -1 kpc than at ∼45 h -1 kpc, though  II] contamination and dust extinction. The green, red, and blue dots mark the SFR in the wide, secure, and merging Pairs in our sample.
The "results" phase The results phase corresponds both to the description of measurements made during the mission as well as their interpretation, which could continue indefinitely, as in the case of the discussion of Viking data. This phase essentially comprises the scientific return of the mission and aims to report, explain and discuss the measurements obtained with the on-board instrumentation. For this scientific phase, geological and chemical analogue samples are used together with sites of geomorphological and petrological analogy as comparison material to enable the interpretation of datasets coming from one or several instruments. This ultimately validates or invalidates hypotheses (e. g. pebbles from Mojave Desert used to interpret Curiosity observations, as mentioned above). This phase ranges from the end of phase E to the end of phase F according to ESA definitions and corresponds to the data analysis phase according to NASA definitions (Table  1 ).
Objective: To evaluate the accuracy of the French health administrative database to describe patients' medication and primary care visits, in the context of a transitional care intervention including an in-hospital medication reconciliation followed by a structured community follow-up by the patient's general practitioner and pharmacist. Design: A retrospective cohort study of older persons enrolled in the transitional care intervention between January 1st, 2015 and December 31st, 2018. Results: Only 46. 1% of the community follow-up were timely billed, in the 3 months after the patient discharge. The sensitivity of the health administrative database to identify medications was 90. 0%. Its positive predictive value was 50. 1%. Conclusion: This study reveals that the French health administrative database was poorly reliable to identify both community follow-up and chronic medications.
Introduction In many countries, health administrative databases have been used to conduct surveillance and research activities: i. e. describing diseases' prevalence, and incidence, or patients' trajectories  [1]. Their accuracy to describe these indicators varies widely depending on the type of data, their source and use  [2]. The accuracy of the health administrative database to describe patients' medication and primary care visits, in the context of a transitional care intervention, has never been assessed. The PAERPA (Personnes âgées En Risque de Perte d'Autonomie) intervention was an ideal setting to perform such an evaluation. This intervention was an innovative integrated care program including actions at the macro-, meso-and micro-levels  [3]. The PAERPA intervention included a transitional care intervention involving an in-hospital medication reconciliation followed by a structured community follow-up by the patient's general practitioner (GP) and pharmacist  [4].
Study design and ethics approvals We analyzed a retrospective cohort of older persons enrolled in the PAERPA intervention between January 1st, 2015 and December 31st, 2018 in the Valenciennois-Quercitain area in France. Informed consent was obtained from every participant: patients and health-care professionals. In line with the ethic approval granted by the French government, the data were accessed through the Hauts-de-France Regional Health Authority after registration with the French National Data Protection Commission (Commission Nationale de l'Informatique et des Libertés).