dataesr/scientific-paragraphs-categorization huggingface dataset:
Software We implemented in prolog language two constraints-based search algorithms (i. e. solvers) to solve FIC/DK-P. Algorithmic details as well as implementation are available in Appendix B. The first solver -Solver 1 -evaluates the validity of a solution after its full assignment. We suppose that the behavior of Solver 1 is somewhat representative of an educational stakeholder's behavior trying to solve FIC/DK-P: the evaluation of the solution will be carried out at the end of a full assignment for the sake of convenience. Yet, we face an important combinatorial explosion that forced us to drastically reduce the dimensions of the datasets used. At the end of the experiment, we used C ∈ [24; 100], K ∈ {10; 20; 30} and S ∈ {2; 3}, with the objective of the student expressed as 2 requisites and the number of academic terms T being equal to 6 or 10: above these parameters the problem becomes intractable, exceeding the time limit of 12 hours we fixed (considering that the problem is supposed to be solved for several hundred or thousand students in a real context).
Discussion The experimental results we present are all based on simulated data produced from one real-life dataset we extrapolated, which may have introduced some biases in them. The first point to which we wish to draw attention on the representativeness of the simulated data, as we cannot guarantee their soundness nor accuracy compared to reallife situations: doing so is equivalent to solving FIC/DK-P. Consequently, we could not guarantee that, for a given objective, a solution exists. This may be one of the reasons regarding the difficulty of maximizing the fitness metric related to the objective of a student ν 2. Yet, this is an interesting point to consider, as it clearly illustrates how, for institutions, the task of certifying that the academic outcomes they propose are aligned with the courses is difficult.
Another point that seems important to consider is the difficulty of gathering courses and knowledge information in institutions that are not fully engaged in this kind of practice. This requires raising teachers' awareness regarding how to describe their teachings. An important future step to further study the problem could involve the creation of a comprehensive real-life dataset that is shared within the community. This effort could be facilitated by developing a collaborative procedure for collectively defining a standardized description of the dataset. Benchmarking such a dataset will be worthwhile as it will allow a common comparison point for the entire community that can therefore be deployed in a real-life context, leading to the very first feedback from the education stakeholders.
For now, we are aiming to create a data warehouse containing simulated datasets. Our objective in this paper was not to find the best solution for a given instance of FIC/DK-P, but to prove that near-good solutions can be computed and to investigate FIC/DK-P. As a consequence, we used in our GA a mono-objective method which may be not, in retrospect, the most suited for solving the problem: we show by studying our experimental results that this problem is multi-dimensional. A better approach could be to work on multi-objective optimization and identifying the Pareto front of the problem which could help to find the best combination. Incidentally, we also question the independence of the parameters of the GA (e. g. mutation, crossover) regarding the instance of the problem. In any case, we advocate that it should be the education stakeholders that make the decisions process, supported by this kind of decision-making tool, and not the other way around.
e. , retrieval algorithm; see also the PRF): the reprocessing dataset v1. 2. 2 from 30 April 2018 to 17 October 2018, the offline v1. 2. 0 from 17 October 2018 to 28 November 2018, the offline v1. 2. 2 from 28  November 2018 to 20 March 2019, v1. 3. x from 20 March 2019 to 29 November 2020, and v1. 4. 0 from 29 November 2020 until March 2021, inclusive. The changes introduced from processor v1. 2. 0 to v1. 3. 2 are relatively minor and do not have a large impact on NO 2. Therefore, we consider the time series from April 2018 to October 2020 as being rather stable. The upgrade to version 1. 4. 0 on 29 November 2020 (for the offline product) brought only one change, namely an update of the FRESCO cloud retrieval, which led to an overall decrease in the effective cloud pressure of about 5 %. Because of the large sensitivity of the NO 2 retrieval to this quantity, this resulted in a considerable change and general increase in NO 2 in the more polluted regions  (van Geffen et al.
, 2022; Riess et al. , 2022). The datasets were filtered with the "qa_value" quality parameter provided in the data product, and only observations with a qa_value > 0. 75 were kept to produce the results described below. This filtering choice can be seen as a cloud mask, since it removes the cloud-covered scenes (with a cloud radiance fraction > 0. 5) and some more uncertain retrievals. The TROPOMI mission includes a routine validation effort (http://mpc-vdaf. tropomi. eu, last access: 20 May 2022) with 3-monthly updates of the validation report for the operational products  (Lambert et al. , 2021). The main results of the validation against MAX-DOAS, Pandora, and Système D'Analyse par Observations Zénithales (SAOZ) instruments is summarised in  Verhoelst et al. (2021) , although the averaging kernel is not used in that work.
Comparisons between TROPOMI and CAMS By averaging the regridded maps of all TROPOMI orbits and model data within a given period, we are able to perform comparisons of tropospheric columns for that period. Note that the maps presented in the figures in this section do not correspond to the whole CAMS regional domain of the target grid, but are magnified to a more central part of the European domain, and all figures include cells that contain valid TROPOMI observations, based on the criteria defined in Sect. 4. 2. Moreover, regional modeled NO 2 fields in this section originate from the first forecast day and the near-realtime (NRT) analyses for the day before.
g. , differences in activity during the examined days or in the emission injection characteristics). Ship tracks can be seen in both TROPOMI and CAMS fields but are generally more prominent in the CAMS fields (e. g. , in the Bay of Biscay or the North Sea). TROPOMI also seems to indicate higher background values in summer. Monthly averages of regridded column fields offer wider spatial coverage and a qualitatively different view of the observed and modeled features. The maps in Fig. 6  present the comparison of the mean TROPOMI tropospheric NO 2 columns S5P with CAMS-RG-A for the months of July 2018 and January 2019. Similar to the daily fields, CAMSregional data in this section are also based on the first day of the 4 d forecasts. CAMS columns appear to be capturing the observed locations of high NO 2 columns well over densely populated regions like the Benelux area, Po Valley, and other large European cities.
Time series Figure  7  depicts the time series of monthly mean S5P and CAMS-RG-A vertical column densities for nine major European cities, while Fig. 8  shows the corresponding time series for the domains of Fig. 3  from May 2018 to March 2021. For this long time series, the CAMS-regional data utilized were the NRT analyses, as this is the regional product used in the European L2 TROPOMI product, as detailed in Sect. 6. The spatial averaging required for the time series of the cities (Fig. 7 ) was performed for 7 × 5 cell domains (0. 7 • × 0. 5 • ) for all the cities, which corresponds to areas of roughly 70 × 50 km 2 centered at the respective city centers. This comparison reveals remarkably similar values between the S5P and CAMS-RG-A columns for some of the cities in southern Europe (Madrid, Rome, and Istanbul), while discrepancies are larger in most of the rest, especially in the wintertime when CAMS-derived column densities almost always acquire higher values.
Relative differences are up to 15 % for most cities in July but up to 50 % for most cities in November. Correlations are typically high, except for London in July and Helsinki in November. For the months of July and November 2018, Figs. 7 and 8 also depict the model spread of the CAMS-RG-A columns in the form of solid green vertical bars. Note that the amount of data involved in the actual regional model output, download time, storage space, and the computational cost required for processing 7 to 11 models has prevented us from calculating the spread for the full length of the time series in these plots. Furthermore, for cities, the spread is also clearly lower in July than in November in the selected domains, but the S5P column values still mostly lie within the range since the comparison is in any case more favorable during the summer.
A European TROPOMI NO 2 level 2 product The importance of using high-resolution realistic profiles as a priori has been recognized by several groups, resulting in dedicated regional retrieval products (M. Liu et al. , 2020; Griffin et al. , 2019; Laughner et al. , 2019)  for the USA and East Asia. Based on the combination of the CAMS-regional NO 2 profiles up to 3000 m altitude and IFS (compo) above, we have generated a new S5P-RG level 2 TROPOMI NO 2 product, using the approach described in Sect. 4. 2. For this product, we chose to use the regional ENSEMBLE for the NRT analysis rather than the forecasts in order to benefit from its better performance  (Douros et al. , 2020). For this new product, horizontal regridding is performed with the TROPOMI grid for the orbit as the target. This new product is an extension of the L2 product files with three new fields added to the existing data product.
The most relevant extra field is the one containing the S5P-RG data but the original (S5P) is also still available. Data files have been cropped to remove parts of the orbits with no overlap with the CAMS European domain, reducing the size of the dataset by about a factor 15 compared to the original L2 dataset. In general, we find that the 0. 1 • high-resolution CAMSregional a priori profile increases the dynamical range in NO 2 vertical column density (VCD) values. More specifically, in the emission hotspots, NO 2 values are typically increased by between 5 % and 30 %, but the increase strongly depends on the location and time.
In this study, we have used nine MAX-DOAS and six Pandora stations (see Table  4 ) that are within the spatial scope of the S5P-RG product. In addition, we compare the operational TROPOMI product and the CAMS-RG-I product with the same reference dataset. This allows for a double-delta validation, i. e. , S5P-RG vs. S5P and S5P-RG vs. CAMS-RG-I. Practically, the CAMS-RG-I tropospheric columns can be reconstructed from the fields in the new product by multiplying the smoothed CAMS column (CAMS-RG-A) with the ratio of the operational S5P tropospheric air mass factor (AMF) over the S5P-RG tropospheric AMF, with all the relevant information made available in the new product. This follows that, by rearranging the second line of Eq. (  1 ), we obtain C I ≡ l x cams l = M(x tm5 ) M(x cams ) l A trop l x cams l = M(x tm5 ) M(x cams ) C A.
(4) In order to focus on the tropospheric VCD, tropospheric columns are estimated from the Pandora total columns by subtracting the S5P stratospheric estimation, as done in  Pinardi et al. (2020). This is indicated by PGNtropo. The filter and colocation processing follows  Verhoelst et al. (2021). Only satellite pixels with qa_value > 0. 75 and covering the ground-based instrument location are kept. Regarding PGN data, only the highest-quality label (0 and 10) is used. MAX-DOAS data are retained if the data within ±1 h of overpass time are available; the interpolated value at overpass time is then used. PGN data are retained if the data within ±0. 5 h of overpass time are available; the average value is then used. Figure  15  presents the results of the validation as density scatterplots of the three datasets (the operational S5P, the S5P-RG, and the CAMS-RG-I shown as columns in the figure) versus the MAX-DOAS data (upper row) and the PGN data (lower row), where the co-located data are merged over all stations.
The correlation is slightly better for S5P than for S5P-RG, while it is decidedly lower for CAMS-RG-I. -The dispersion from the regression line is lower for S5P than for S5P-RG, which in turn is lower than for CAMS-RG-I. Figure  16  is based on the same data, separated per station, that shows both the relative bias (median relative difference) for each dataset and the bias change with respect to S5P or CAMS-RG-I. Blue bars indicate a reduction in the bias, while red bars indicate an increase in the bias. At all stations, except the PGN sites of INOE, S5P-RG presents a reduction in the bias from 5 % to 18 % compared to S5P, showing the impact of the a priori profile shapes used in the satellite retrievals. Bias changes in S5P-RG compared to CAMS-RG-I can be positive or negative, are highly station dependent, and can be much larger in absolute value.
During summer, the tropospheric NO 2 columns, based on the CAMS ENSEMBLE having applied the satellite averaging kernels, agree quantitatively to the corresponding columns from TROPOMI, especially over major European cities. Background values in summer, however, are found to be significantly higher in the TROPOMI dataset, either indicating issues with the modeling of soil emissions in the CAMS models or with the separation of troposphere from the stratosphere in the retrieval. In winter, the average ENSEMBLE-based column amount is found to be significantly and systematically higher, and the difference seems to increase with latitude. Possible reasons for this, including both larger retrieval uncertainties and modeling deficiencies of NO 2 simulations in winter, were discussed. Validation against surface NO 2 concentrations performed within CAMS does not confirm, however, this different behavior depending on latitude, a fact which points to the complementary value of satellite-derived tropospheric columns as a means for validating atmospheric composition models.
This characteristic is further supported by calculating the ratio of the TROPOMI column with the replaced a priori over the native TROPOMI column at the locations of the global MAX-DOAS network of remote sensing instruments. Most urban locations indicate a column enhancement of at least 10 % when replacing the TM5-MP a priori of the retrieval with the merged CAMS profile. Remote locations, however, point to values of the ratio being lower than unity, which means that the replacement of the a priori corrects to lower column values. These corrections seem to be robust in time, with no obvious seasonality. Comparisons with MAX-DOAS and PANDORA data indicate that the overall bias of tropospheric NO 2 when replacing the a priori is improved when compared to that of the standard, operational S5P product.
Code and data availability. The bulk of the code used in this paper is available on Zenodo (https://doi. org/10. 5281/zenodo. 7016483; Douros, 2022). Horizontal interpolation was done using the regridding capabilities of the Earth System Modeling Framework (ESMF; https://earthsystemmodeling. org/regrid, last access: 20 May 2022). TROPOMI L2 datasets used in this paper are made available operationally through the ESA Sentinel-5P data hub (https:// s5phub. copernicus. eu, last access: 20 May 2022), while the new L2 product described in Sect. 6 is made available through the TEMIS portal (https://www. temis. nl/airpollution/no2col/no2_euro_ tropomi_cams. php, last access: 20 May 2022). Since the conception and writing of this paper, the data in TEMIS have been updated using the S5P-PAL NO 2 dataset (https://data-portal.
s5p-pal. com/ products/no2. html, last access: 20 October 2022), which is based on version 2. 3. 1 of the processor. Data for the new L2 product used in this paper have, however, been retained and can be made available, upon request to the corresponding author, to interested users. CAMS model data, both for the regional models and IFS (compo) were retrieved from the CAMS Atmosphere Data Store (https: //ads. atmosphere. copernicus. eu, last access: 20 May 2022) and its predecessor hosted by Météo-France. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-16-509-2023-supplement. Author contributions. JD developed the code for the comparisons between TROPOMI and CAMS and for the production of the new L2 product. JD and HE were responsible for the development of the proposed methodology and are the main authors of the paper.
SC and GP were responsible for the comparison with the MAX-DOAS and the Pandora observations. VHP and AC contributed to the CAMS modeling part. All authors reviewed and improved on the paper. contributes to the Network for the Detection of Atmospheric Composition Change (NDACC) and is publicly available. The PGN data were collected from Stelios Kazadzis (PMOD/WRC), for Athens, Jari Hovila (FMI), for Helsinki, Anca Nemuc (INOE), for Mȃgurele, Martin Tiefengraber (Luftblick), for Innsbruck, and Stefano Casadio (ESA), for Rome CNR and Rome Sapienza, while central data processing is done by Martin Tiefengraber (Luftblick). PGN is a bilateral project supported with funding from ESA and NASA, and the data are available publicly.
Acknowledgements. Sentinel-5 Precursor is a  European Space Agency (ESA)  mission on behalf of the  European Commission (EC). The TROPOMI payload is a joint development by  ESA  and the  Netherlands Space Office (NSO). The Sentinel-5 Precursor ground segment development has been funded by  ESA  and with national contributions from the  Netherlands, Germany , and  Belgium. This work contains modified Copernicus Sentinel-5P TROPOMI data (2018)(2019)(2020)(2021), processed in the operational framework or locally at  KNMI , with post-processing for validation purposes performed by  BIRA-IASB. Part of the reported work was carried out in the framework of the  Copernicus Sentinel-5 Precursor Mission Performance Centre  ( S5P MPC ), contracted by the  European Space Agency (ESA/ES-RIN ; contract no.
The authors are also thankful to  Tijl Verhoelst  ( BIRA-IASB ), for in-depth discussions on NO 2 validation methods and uncertainties. Copernicus Atmosphere Monitoring Service (CAMS) is implemented by the  European Centre for Medium-Range Weather Forecasts  and its contractors on behalf of the  European Commission Directorate-General for Defence Industry and Space sector (DG DEFIS)  as a component of the  European Union's flagship Space program Copernicus. Financial support. This research has been supported by the  European Space Agency (ESA/ESRIN ; contract no. 4000117151/16/I-LG ) and  BELSPO  and  ESA  through the  ProDEx  project  TROVA-E2  (grant no. PEA 4000116692 ). Review statement. This paper was edited by  Slimane Bekki  and reviewed by two anonymous referees.
Mass spectrometry has become an indispensable tool in the life sciences. The new major version 3 of the computational framework OpenMS provides signi cant advancements regarding open, scalable, and reproducible high-throughput work ows for proteomics, metabolomics, and oligonucleotide mass spectrometry. OpenMS makes analyses from emerging elds available to experimentalists, enhances computational work ows, and provides a reworked Python interface to facilitate access for bioinformaticians and data scientists.
Main Mass spectrometry (MS) is an analytical technique with a wide range of biological and medical applications, which can produce data on the terabyte scale demanding large-scale high-throughput analysis. Computational mass spectrometry seeks to distill MS data into insights that advance understanding in the life sciences. Here we proudly present OpenMS 3, which is a major leap forward in support for omics data beyond proteomics (e. g. , metabolomics and RNA/DNA), support for work ow systems, and an improved user experience. OpenMS is an actively maintained, open-source software project that has aided mass spectrometrists with their data processing since 2002. It started as a C++ library and has expanded to include applications, Python libraries, work ows, and documentation repositories. Since the release of OpenMS 2.
Read Full License Additional Declarations: Yes there is potential Competing Interest. L. H. is an employee of Laminlabs. H. W. is an employee of Storm Therapeutics. L. B. is an employee of BioNTech SE. L. N. is an employee of OpenMS Consulting. O. K. , T. S. and S. W. are o cers in OpenMS Inc. , a non-pro t foundation which manages the international coordination of OpenMS development. Version of Record: A version of this preprint was published at Nature Methods on February 16th, 2024. See the published version at https://doi. org/10. 1038/s41592-024-02197-7. foundation for interoperability is the use of open standard le formats. Users can analyze their data with ready-to-go pipelines. Developers can use the OpenMS library to develop e cient new tools and algorithms in C++. OpenMS uses modern software engineering concepts emphasizing modularity, reusability, and extensive testing.
The library contains an extensible GUI module using Qt, which forms the basis for a powerful data viewer  2. OpenMS offers native C++ compiler support on Windows (MSVC), Linux (Clang and GCC), and macOS (Clang). The permissive BSD license encourages academic and commercial use, facilitating collaborations and joint research. OpenMS is hosted on GitHub, where continuous integration/testing and code reviews ensure that contributions meet high-quality standards. The SoftWipe 3 score of the whole library is 6. 9, which puts OpenMS at rank 7 of 48 bioinformatics tools in terms of software quality (see Supplemental Material for details). Active communication between developers and with end users is a cornerstone of the project; this is facilitated by various online tools (Discord, Gitter, mailing lists, conference calls) and annual meetings.
To help new users explore and quickly become productive with OpenMS, the website and documentation 4 (Figure  S1 ) were modernized with the help of professional technical writers. Built on top of the library, pyOpenMS gives access to algorithms and tools using the Python scripting language, enabling interaction with Python libraries for data science, machine learning, and data visualization. pyOpenMS is easy to install through pip or Conda and scripts can be written by nonexperts. This makes pyOpenMS more accessible to experimentalists and lowers the barrier of entry for the OpenMS ecosystem. Python support is highly useful for prototyping new algorithms and for easy deployment as web applications. In addition, pyOpenMS is a great teaching tool, with a revamped tutorial (Figure  S2a ) and with the Binder integration 5 , which provides access to pyOpenMS using only a web browser (Figure  S2b ).
New developments in bottom-up proteomics include the quantMS 6 Next ow work ow for label-free quanti cation that generates tables compatible with MSstats 7 for statistical analysis. Other additions are the protein-protein crosslinking search tools OpenPepXL 8 and OpenPepXLLFfor isotopically labeled and label-free crosslinkers. Furthermore, existing tools (e. g. , OpenSWATH) have been updated to support more e cient le formats (e. g. , SQLite) and ion mobility analysis. Recently, various tools and suites for top-down proteomics have been added to OpenMS. FLASHDeconv 9 enables accurate deconvolution of top-down MS datasets orders of magnitude faster than other state-ofthe-art tools such as TopFD  10  and Xtract (Thermo Fisher Scienti c). The latest version of FLASHDeconv 11 also features accurate deconvolution of MS/MS spectra, deconvolution false positive rate (FDR) estimation, and deep-learning-based scoring.