dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the content of field "V9" was amended to include more precise information about whether region V9 is complete ("yes -complete"), missing some positions at the 3' end ("yes -partial"), or was excluded, with six possible reasons why ("no -missing", "no -too incomplete", "nochimera", "no -bad quality", "no -deletion in V9", "no -Ns in V9"). and Research of the Ministry of Economy and Knowledge, and from "la Caixa" Foundation (ID 100010434), with the fellowship code LCF/BQ/PI19/11690008. This work was supported by the French Government 'Investissement d'Avenir' program OCEANOMICS (ANR-11-BTBR-0008) and the ABiMS computing cluster at the Station Biologique de Roscoff, France. Ward GM, Neuhauser S, Groben R, Ciaghi S,  Berney C, Romac S, Bass D. 2018   Figure  2. Plots of the similarity scores resulting from the self-annotation of EukRibo, illustrating the existence of maximum similarity gaps between the supergroup, taxogroup1 and taxogroup2 taxonomic levels that can be applied to taxonomic annotation of ASVs from metabarcoding datasets.
The extracted V4 region of each EukRibo sequence was compared to all others by pairwise alignment using VSEARCH. The similarity score of each pairwise comparison was mapped along the Y-axis according to its best similarity score against any other sequence in the database (i. e. including those from the same lineage), while the X-axis shows the % difference with the best similarity score against any sequence in another supergroup, taxogroup1, or taxogroup2, respectively. Figure Legends.
We aimed to compare measurements from three of the most widely used software packages in the literature and to generate conversion algorithms for measurement of the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) between SIVA and IVAN and between SIVA and VAMPIRE. We analyzed 223 retinal photographs from 133 human participants using both SIVA, VAMPIRE and IVAN independently for computing CRAE and CRVE. Agreement between measurements was assessed using Bland-Altman plots and intra-class correlation coefficients. A conversion algorithm between measurements was carried out using linear regression, and validated using bootstrapping and root-mean-square error. The agreement between VAMPIRE and IVAN was poor to moderate: The mean difference was 20. 2 µm (95% limits of agreement, LOA, -12.
2-52. 6 µm) for CRAE and 21. 0 µm (95% LOA, -17. 5-59. 5 µm) for CRVE. The agreement between VAMPIRE and SIVA was also poor to moderate: the mean difference was 36. 6 µm (95% LOA, -12. 8-60. 4 µm) for CRAE, and 40. 3 µm (95% LOA, 5. 6-75. 0 µm) for CRVE. The agreement between IVAN and SIVA was good to excellent: the mean difference was 16. 4 µm (95% LOA, -4. 25-37. 0 µm) for CRAE, and 19. 3 µm (95% LOA, 0. 09-38. 6 µm) for CRVE. We propose an algorithm converting IVAN and VAMPIRE measurements into SIVA-estimated measurements, which could be used to homogenize sets of vessel measurements obtained with different software packages.
All of them include three well-known parameters summarizing vessel widths around the optic disc: CRAE (central retinal artery equivalent), CRVE (central retinal vein equivalent), and arteriovenous ratio (AVR). However, the software packages use different algorithms for vessel segmentation, for vessel labeling (artery or vein), and for estimating vascular diameter. Two additional features are estimated with SIVA and VAMPIRE but not with IVAN: tortuosity and fractal dimension. Previous studies suggest poor-to-moderate concordance between these software packages. A comparison between SIVA and VAMPIRE on 655 images indicated poor-to-limited agreement for all parameters (0. 16-0. 41) and the presence of proportional and systematic bias in the majority of the parameters  [20]. IVAN, SIVA, and RA (Retinal Analysis) have been compared on a series of 120 retinal photographs, but the intra-class correlation to assess concordance was not reported  [21].
Nevertheless, the authors showed that IVAN yields significantly larger retinal vessel measurements than SIVA and they proposed an algorithm to convert IVAN measurements into estimated SIVA measurements. This approach seems attractive for pooling data from different studies. The poor concordance that has been reported implies that the software packages are not interchangeable. There is a need to clarify the quantitative differences between their measurements, which may lead to different conclusions in statistical studies using the same image set. This calls for a systematic and structured comparison of these three software applications. The primary aim of our study was to determine the agreement of CRAE, CRVE, and AVR estimates from the three software tools. Our secondary aim was to propose an algorithm converting measurements between these packages.
Between VAMPIRE and SIVA The mean difference was 36. 6 µm (95% LOA, -12. 8-60. 4 µm) for CRAE measurements, 40. 3 µm (95% LOA, 5. 6-75. 0 µm) for CRVE measurements, and 0. 037 (95% LOA, -0. 071-0. 15) for AVR values. The correlation for CRAE and AVR between each software was poor (ICC = 0. 37 and 0. 38, respectively) and moderate for CRVE (ICC = 0. 47). A significant proportional bias was observed for CRVE and AVR (p < 0. 001) but not for CRAE (p = 0. 09).
Between IVAN and SIVA The mean difference was 16. 4 µm (95% LOA, -4. 25-37. 0 µm) for CRAE measurements, 19. 3 µm (95% LOA, 0. 09-38. 6 µm) for CRVE measurements, and 0. 015 (95% LOA, -0. 09-0. 11) for AVR values. The correlation for CRAE and AVR between each software was good (ICC = 0. 69 and 0. 61, respectively) and excellent for CRVE (ICC = 0. 83). A proportional bias was observed for all the retinal vessel parameters (p < 0. 001 for all). For each pair of packages and for each parameter, the presence of systematic bias was demonstrated by the significance (p < 0. 001) of one-sample t tests comparing mean differences and zero value. To determine a conversion method between measurements from different packages, we computed linear regression relationships for CRAE and CRVE measurements. 4  describes the comparisons between SIVA parameters and VAMPIRE-derived SIVA parameters or IVAN-derived parameters.
Discussion Our study analyzed the agreement and correlation between CRAE, CRVE, and AVR as measured by three widely used software tools-IVAN, SIVA, and VAMPIRE-with 233 fundus camera images of 133 retinas of healthy adults. Our analysis showed an excellent agreement between SIVA and IVAN, but a poor-to-moderate agreement between VAMPIRE and the two other software tools. In addition, we proposed a simple method for converting the measurements from one tool into estimates from another. The evaluation of software tools such as SIVA, IVAN, and VAMPIRE can follow two different approaches. First, one can concentrate on morphological measurements of the retinal vascular tree, typically vessel diameters (as carried out here), tortuosity, junction/bifurcations, and fractal dimension. Second, one can study the association between vascular parameters and systematic parameters, or disease state or risk.
Interestingly, when comparing VAMPIRE and other software packages, the correlation was found to be higher for CRVE than for CRAE. This could be explained by the easier recognition of the venule edges by each software, due to their larger diameter and their more contrasted appearance in a retinal photograph. In our recent study comparing retinal photographs and adaptive optics (AO) imaging, we demonstrated a better agreement and correlation of vein measurements using IVAN when compared with the gold standard AO  [34]. To counter the consequences of the discrepancy between measurements taken by different packages, we have proposed a conversion algorithm based on linear regression. The good correlation between SIVA and IVAN values of CRAE, CRVE, and AVR make this choice sensible, but less so if VAMPIRE values are involved.
We did not use splitsampling for internal validation as reported in other publications  [21]  but we performed the bootstrapping procedure described by Labarère et al. [32]. The robustness of the algorithms is well assessed through the bootstrapping procedure. Between SIVA and IVAN, our algorithms are not strictly comparable to the algorithms previously reported (IVANderived SIVA CRAE = 0. 7176 × IVAN-measured CRAE + 34. 3984 IVAN-derived SIVA CRVE = 0. 7102 × IVAN-measured CRVE + 44. 8717)  [21]. This can be explained by many factors, such as the retinal photograph resolution and compression, which could differ in the samples studied. The influence of these two parameters on IVAN measurements has been studied previously and found to be considerable  [35]. The aforementioned findings highlight the need for standardization of retinal vascular imaging with fundus cameras (resolution, compression, size, instruments, acquisition protocol, quality among others) if we are to expect similar conclusions from a statistical analysis of measurements obtained with different software packages.
Third, neither agreement nor correlation among packages attests to the accuracy of measurements with respect to objective ground truth. This kind of study has been reported elsewhere for the three software packages considered here. Fourth, SIVA and VAMPIRE generate a large number of parameters and we have studied only CRAE, CRVE, and AVR. Since several other parameters (e. g. , tortuosity, fractal dimension) as well as width measurements in regions other than Zone B are used in biomarker studies, a more extensive comparison would be necessary for a proper assessment of the concordance between the three packages. Fifth, our sample is modest in size and comprises adults with healthy retinas. Larger studies including statistical adjustment modeling potentially important factors (e. g. , camera operator, image quality, patient characteristics etc.
Conclusions In conclusion, our study on the agreement and correlation of measurements from the three most widely used software tools for retinal biomarkers found an excellent agreement between SIVA and IVAN and a poor-to-moderate agreement between VAMPIRE and the two other software tools. Comparing measurements of the retinal vasculature obtained from different software tools remains a challenge requiring, arguably, a considerable standardization effort on algorithms, image acquisition protocols and quality at least. Standardization would guarantee not only consistent measurements but also, and importantly, comparable findings in terms of biomarkers for specific conditions obtained from statistical analysis. In our study, a solution suggested by our findings is to convert IVAN and VAMPIRE measurements into SIVA approximate equivalents before use for pooled data analysis.
Because we do not know how to compute the S 2 value in DFT, we should formulate this question -whether or not we have unphysical symmetry breaking -in terms of finding molecular properties that could report on it. The same concerns apply to spatial symmetry breaking. One example of how one may approach this problem is a charge-transfer system, such as (He) 3 + or the charged ethylene dimer. Charge localization is very sensitive to Hartree-Fock-like symmetry breaking and also to the SIE. The comparison of charge localization patterns against high-level reference data can inform us whether symmetry breaking is real or artificial; some useful examples can be found in ref. 415. 3. 6. 12 Baerends. In contribution (3. 6. 6), the ''challenge'' is put forward that, with the dissociating H 2 molecule as an example, one would never see any symmetry breaking if one employs the exact exchange-correlation functional within Kohn-Sham DFT.
Xu. There is an ongoing effort devoted to the development of reference data sets that are sufficiently accurate for benchmarking functional performance. 537, 538 A recent progress in the community is the emergence of large data sets for the main-group chemistry -for example, the MGCDB84 set with about 5000 data points maintained by Head-Gordon's group  273  and the GMTKN55 set of Grimme's group with about 1500 data points. 336 Comprehensive benchmarking of existing DFAs for main-group chemistry has become a reality, numerically validating the concept of Jacob's ladder of Kohn-Sham DFT by demonstrating that a higher-rung DFA is, in general, more accurate than a lower-rung DFA. 336 0] [541]  Large transition-metal test sets with accurate reference data are urgently needed. It is not merely important for benchmarking Kohn-Sham DFAs, but also fundamentally important for understanding the limitations of current Kohn-Sham DFAs for strongly-correlated systems as mentioned in contribution (3.
Based on the experience acquired in the literature with O(N) methods implemented using plane waves, wavelets, or other localized basis sets, it appears that methods with controllable accuracy might come from the development of real-space based techniques, which would also require the development of specific pseudopotentials for periodic DFT calculations. 718. 6. 7 Cances. What is the error in the output of DFT codes relative to the exact value of a chemical or physical quantity of interest (e. g. , the dissociation energy of a molecule, the bulk modulus of a material)? This question is obviously of major importance and is usually addressed by comparing experimental and computational results on large databases. However, such statistical analyses do not really answer legitimate questions of most users, which can be formulated as follows: ''What will be the error for the specific system I am interested in if I use this code, with these numerical parameters (basis set/energy cutoff, convergence thresholds, etc.
systematically improvable: provide detailed information on how to increase the accuracy at the lowest cost. Extending these techniques to the nonlinear Schro ¨dinger and Kohn-Sham equations is work in progress. 723 Such estimators would allow the computer program to choose adaptively, in a black-box manner, the best numerical parameters to reach a given numerical accuracy at the lowest computational cost (error balancing). Error balancing would be particularly useful for building large databases for ML, requiring hundreds of millions of single-point DFT calculations. 724 et us finally discuss the model error. For wave-function methods, it is in principle possible to estimate this component of the error by a careful mathematical analysis of the residual  Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form.
Care should of course be taken that, in the end, the researcher comes to a point where the link proposed by the ML ansatz provides him/her with a reasonable insight into the problem. 4. 7. 4 Grimme. The primary purpose of Kohn-Sham DFT for AI/ML approaches seems to be the efficient generation of the required huge amount of reasonably accurate reference data (mostly energy and forces). 4. 7. 5 Chermette, Adamo, and Ciofini. AI will be also involved in the building of exchange-correlation functionalsfor example, by following Perdew's approach of satisfying 17 theoretical constraints; see contribution (3. 1. 2). A paper in this vein by Kirkpatrick and Cohen appeared recently. 725. 7. 6 Galli. I would like to mention DFT-based deeppotential MD (DeePMD) by Robert Car and Weinan E's group in Princeton  717  and machine learning dielectric screening for the simulation of excited state properties of molecules and materials,  726  which may eventually be used also to derive dielectric-dependent hybrid DFAs.
g. , PySCF, 752 Psi4,  753  Quantum ESPRESSO  754  ) and commercial (e. g. , ORCA,  755  ADF, 756 Molpro 757 ). Furthermore, the library is applicable not only to Gaussian basis sets, but also to plane waves, adaptive grids, and finite-element representations. Libraries and modules such as these not only provide high performance, but also improved reproducibility and standardization, both of which are becoming more vital as the complexity of our models advances. To that end, emerging standards and tools for sharing computational results will similarly grow in importance. In the materials-science domain, for example, this has long been underway with community-driven resources such as the Materials Project 758 as a paradigm, although the standardization of the content of materials databases is still under development within that community.
In the computationalchemistry domain, new tools such as the Quantum Chemistry Schema (QCSchema) and Quantum Chemistry Archive (QCArchive)  759  (an open, public-facing database of computational results) developed by the Molecular Sciences Software Institute (MolSSI)  760, 761  would allow much greater interoperability between codes by facilitating standards for data sharing. In addition, the Simulation Environment for Atomistic and Molecular Modeling (SEAMM),  762  also under development by the MolSSI, provides a lightweight, Python-based plug-in environment for complex, shareable workflows, which will permit sophisticated computations involving multiple community code components in a fully reproducible and publishable manner. The broader the adoption of standards and tools such as these, the faster our community will be equipped to handle more complex and important scientific challenges.
The numerical support could be put in a database that can be searched by automatic tools. 5. 2. 6 Trickey. Again we are faced with generic challenges in physical science. Funding pressures are one. Competition among publishers to have the most exclusive journals is another. Emphasis is on the allegedly spectacular. These influences combine to make it hard to publish careful, incremental advances, let alone negative results. Within the DFT community, maybe we should urge editors to accept the publication (hence, also, respectful refereeing) of careful presentation of negative results about well-motivated, well-grounded attempts at advancement? 5. 2. 7 Gori-Giorgi. We should also not forget that the pressure to publish goes hand in hand not only with the competition to obtain funding but also with how we evaluate (young) scientists.
Acknowledgements The authors are indebted to  Steven Berry ,  Durham University , for preparation of the cover art graphics associated with this paper. AK acknowledges support from the  U. S. National Science Foundation (NSF)  through Grant No. CHE-2154482. AL acknowledges support from the  Norwegian Research Council  through Grant No. 287906  (CCerror) and  262695  ( CoE Hylleraas Center for Quantum Molecular Sciences ). AMK acknowledges support from the  Mexican Consejo Nacional de Ciencia y Tecnologı ´a  through Grant No. A1-S-11929. AMT acknowledges financial support from the  European Research Council  under  H2020 /  ERC Consolidator Grant  topDFT (Grant No. 772259 ). CAU is supported by  DOE  grant No. DE-SC0019109  and  NSF  grants No. DMR-1810922  and  DMR-2149082. CD acknowledges funding by the  German Research Foundation (DFG)  through projects  460197019 ,  424709454 , and 182087777 as well as by the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement No.
951786  ( NOMAD CoE ). EAC acknowledges financial support from the  Advanced Scientific Computing Research Program  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences , under Award No. DE-AC02-05CH11231. EC acknowledges support from the  European Research Council (ERC)  under  H2020/ERC  Grant  EMC2 810367. EF thanks  LabEx CSC  (grant number:  ANR-10-LABX-0026-CSC ) and  ANR  ( CoLab  project, grant number:  ANR-19-CE07-0024-02 ) for funding. ERJ thanks the  Natural Sciences and Engineering Research Council of Canada (NSERC)  for financial support. FDP and PG want to acknowledge the  Vrije Universiteit Brussel  for the support through a  Strategic Research Program  (SRP). GG acknowledges  DOE/BES  support through the  Computational Materials Science Center MICCoM.
HCh thanks gratefully  C. Morell  for his continuous support and for thorough discussions. JG acknowledges support from the  U. S. National Institutes of Health  under grant no. GM046736. JPP acknowledges support from the  U. S. National Science Foundation  under grant no. DMR-1939528  and from the  U. S. Department of Energy  under grant no. DE-SC0018331. LK is supported by the  Helen  and  Martin Kimmel Award  for Innovative Investigation and the  Aryeh and Mintzi Katzman Professorial Chair. ML acknowledges support from the  European Research Council (ERC)  under  H2020/ERC Grant MDFT 725528. MLe acknowledges support from the  Julian Schwinger Foundation. MS acknowledges funding from the  European Unions Horizon 2020 Research and Innovation Programme  (No. 951786 , the  NOMAD Center of Excellence)  and from the  ERC Advanced  Grant  TEC1P  (No.
740233 ). NG acknowledges financial support from The  Leverhulme Trust , through a  Research Project  (Grant No. RPG-2016-005 ). PFL thanks the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 863481 ) for financial support. PKC acknowledges  DST, New Delhi, India for the J. C. Bose National Fellowship , grant number  SR/S2/JCB-09/ 2009. PPi acknowledges support from the  Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U. S. Department of Energy  under award no. DE-FG02-01ER15228. SBT is supported by  U. S. DOE  grant  DE-SC0002139  and  NSF  grant  DMR-1912618. TDC was supported by the  Molecular Sciences Software Institute under U. S. National Science Foundation  grants  ACI-1547580  and  CHE-2136142.
LNE-CETIAT liquid flow laboratory is the French Designated Institute for liquid water flow rate from 1 g h -1 to 50 t h -1. Historically, its primary standards are based on the flying start and stop gravimetric method. The best relative expanded uncertainty for liquid mass flow rate is 0. 05% (k = 2). In the scope of the Joint Research Project Metrowamet and its mission to maintain and develop the French standards for liquid flow, LNE-CETIAT has developed and validated a dynamic primary standard for unsteady liquid flow calibration. This paper will first present the developped system, which is composed of a dynamic flow generator and a dedicated measuring system together with its own software for data acquisition and processing. The validation, realized by intra and inter-laboratory comparisons for static and dynamic flows, is presented in the third chapter.
The time acquisition accuracy and traceability is ensured by the calibration of the sampling frequency using LNE-CETIAT's atomic clock, itself calibrated against the French National Time & Frequency (held by LNE-SYRTE) by the GNSS frames comparison method. This calibration method allows for an   The DUT outputs (either pulses, current, voltage, or digital) are synchronized with the reference flow rate, temperature and pressure measurements using a dedicated acquisition system. The expanded relative uncertainty on the reference flow rate is 0. 1% (k = 2) for static (constant) flow, 0. 2% (k = 2) for dynamic (fluctuating) flow. As described previously in Refs. [3, 4] , the uncertainty calculation for the case of dynamic flow is composed of the uncertainty on static flow and the residuals of the linear fit used to calculate the average dynamic flow over the interval of mass data considered, as follows: U dynamic, k=2 = 2* ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ u 2 static + u 2 fit √ (1) With u static the uncertainty on static reference flow measurements and u fit the standard deviation of the linear fit residuals  [3, 4].