dataesr/scientific-paragraphs-categorization huggingface dataset:
As of 2010, according to the poverty line standard issued by the World Bank (US$1. 25 per day), the number of poor people in China has decreased by 150m compared to a decade ago, and only 6% of the rural population has a wage income below the poverty line. Despite the achievements in poverty alleviation, the income gap among Chinese residents has continued to widen. This expansion was moderated in the early 2000s. The China Household Income Survey Data (CHIP) and the China Family Dynamics Tracki ng Survey (CFPS) collected microscopic data for six years from 1995 to 2014. Table  1  shows the Gini coefficient in China over the six years. Income inequality is "inverted U-shaped", with a peak between 2007 and 2010. This paper uses the Theil index and the urban-rural income ratio to measure income inequality.
Regression and analysis Before regression analysis, the panel data were tested by Hausman to determine whether to adopt fixed effect or random effect model. The test results strongly reject the hypothesis that there is no significant difference between the two models, so we choose the fixed effect model. In the regression analysis, the logarithmic descendants of the Thiel index and the financial scale, institution and efficiency of the independent variables are used in the model to eliminate the heteroscedasticity of the data. The regression results of the fixed effect model for 30 provinces and municipalities are shown in table  3 , and the columns correspond to six models in the previous section. By comparing the coefficients of the independent variables in (1)(2), (3)(4), and (5)(6), we can find that the coefficients of the (2)(4) model independent variables are all significant, which indicates that there is a Kuznets effect on income inequality in financial scale and financial structure; However, the coefficients of the quadratic terms of financial efficiency and financial efficiency are not significant in the (5)(6) model.
The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD -data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation, such as temperature and simulation length, and identify model resolution, such as all-atom and coarse-grain.
This leads to a world 1 of 24 where research data has become widely available and exploitable, and consequently new applications based on artificial intelligence (AI) emerged. One example is AlphaFold  (Jumper et al. , 2021) , which enables the construction of a structural model of any protein from its sequence. However, it is important to be aware that the development of AlphaFold was only possible because of the existence of extremely well annotated and cleaned open databases of protein structures (wwPDB Berman et al. (2003) ) and sequences (UniProt Consortium  (2022) ). Similarly, accurate predictions of NMR chemical shifts and chemical-shift-driven structure determination was only made possible via a community-driven collection of NMR data in the Biological Magnetic Resonance Data Bank  (Hoch et al.
In this endeavor, discussions started a few years ago  (Abraham et al. , 2019; Abriata et al. , 2020; Merz et al. , 2020)  and the MD data sharing trend has been accelerated with the effort of the MD community to release simulation results related to the COVID-19 pandemic  (Amaro and Mulholland, 2020; Mulholland and Amaro, 2020)  in a centralized database (https://covid. bioexcel. eu). Specific databases have also been developed to store sets of simulations Albeit previous attempts in the past  (Tai et al. , 2004; Meyer et al. , 2010) , there is, as of now, no central data repository that could host all kinds of MD simulation files. This is not only due to the huge volume of data and its heterogeneity, but also because interoperability of the many file formats used adds to the complexity. Thus, faced with the deluge of biosimulation data  (Hospital et al.
Results With the rise of open science, researchers increasingly share their data and deposit them into generalist data repositories, such as Zenodo (https://zenodo. org), Figshare (https://figshare. com), Open Science Framework (OSF, https://osf. io), and Dryad (https://datadryad. org/). In this first attempt to find out how many files related to MD are deposited in data repositories, we focused our exploration on three major data repositories: Figshare (∼3. 3 million files, ∼112 TB of data, as of January 2023), OSF (∼2 million files, as of November 2022) 1 , and Zenodo (∼9. 9 million files, ∼1. 3 PB of data, as of December 2022; Panero and Benito (2022)). One immediate strategy to index MD simulation files available in data repositories is to perform a text-based Google-like search. For that, one queries these repositories with keywords such as 'molecular dynamics' or 'Gromacs'.
Unfortunately, we experienced many false positives with this search strategy. This could be explained by the strong discrepancy we observed in the quantity and quality of metadata (title, description) accompanying datasets and queried in text-based search. For instance, a description text could be composed of a couple of words to more than 1,200 words. Metadata is provided by the user depositing the data, with no incentive to issue relevant details to support the understanding of the simulation. For the three data repositories studied, no human curation other by that of the providers is performed when submitting data. It is also worth mentioning that title and description are provided as free-text and do not abide to any controlled vocabulary such as a specific MD ontology. To circumvent this issue, we developed an original and specific search strategy that we called Explore and Expand (𝐸𝑥 2 ) (see Fig.
1 -A and Materials and Methods section) and that relies on a combination of file types and keywords queries. In the Explore phase, we searched for files based on their file types (for instance:. xtc,. gro, etc) with MD-related keywords (for instance: 'molecular dynamics', 'Gromacs',  'Martini', etc). Each of these hit files belonged to a dataset, which we further screened in the Expand phase. There, we indexed all files found in a dataset identified in the previous Explore phase with, this time, no restriction to the collected file types (see Fig. 1-A  and details on the data scraping procedure in the Materials and Methods section). Globally, we indexed about 250,000 files and 2,000 datasets that represented 14 TB of data deposited between August 2012 and March 2023 (see Table  1 ). One major difficulty were the numerous files stored in zipped archives, about seven times more than files steadily available in datasets (see Table  1 ).
While this choice is very convenient for depositing the files (as one just needs to provide one big zip file to upload to the data repository server), it hinders the analysis of MD files as data repositories only provide a limited preview of the content of the zip archives and completely inhibits, for example, data streaming for remote analysis and visualization. Files within zip files are not indexed and cannot be searched individually. The use of zip archives also hampers the reusability of MD data, since a specific file cannot be downloaded individually. One has to download the entire zip archive (sometimes with a size up to several gigabytes) to extract the one file of interest. The first dataset we found related to MD data that has been deposited in August 2012 in Figshare and corresponds to the work of Fuller et al.
(Fuller et al. , 2012)  (see Table  1 ) but we may consider the start of more substantial deposition of the MD data to be 2016 with more than 20,000 files deposited, mainly in Figshare (see Fig. 1-B ). While the number of files deposited in Zenodo was first relatively limited, the last few years (2020-2022) saw a steep increase, passing from ) strategy used to index and collect MD-related files. Within the explore phase, we search in the respective data repositories for datasets that contain specific keywords (e. g. "molecular dynamics", "md simulation", "namd", "martini". ) in conjunction with specific file extensions (e. g. "mdp", "psf", "parm7". ), depending on their uniqueness and level of trust to not report false-positives (. i. e not MD related). In the expand phase, the content of the identified datasets is fully cataloged, including files that individually could result in false positives (such as e.
g. ". log" files). (B) Number of deposited files in generalist data repositories, identified by our 𝐸𝑥 2 strategy. a few thousands files in 2018 to almost 50,000 files in 2022 (see Fig. 1-B ). In 2018, the number of MD files deposited in OSF was similar to those in the two other data repositories, but did not take off as much as the other data repositories. Zenodo seems to be favored by the MD community since 2019, even though Figshare in 2022 also saw a sharp increase in deposited MD files. The preference for Zenodo could also be explained by the fact that it is a publicly funded repository developed under the European OpenAIRE program and operated by CERN (European Organization For Nuclear Research and OpenAIRE, 2013). Overall, the trend showed a rise of deposited data with a steep increase in 2022 (Fig.
Thus, as it is, this file is not easily mineable to extract useful information, especially if multiple. xtc and coordinate files are available in one dataset. Interestingly, we found 1,406. trr files, which contain trajectory but also additional information such as velocities, energy of the system, etc. While this file is especially useful in terms of reusability, the large size (can go up to several 100 GB) limits its deposition in most data repositories. For instance, a file cannot usually exceed 50 GB in Zenodo, 20 GB in Figshare (for free accounts) and 5 GB in OSF. Altogether, Gromacs trajectory files represented about 30,000 files in the three explored generalist repositories (34% of Gromacs files). This is a large number from less interesting trajectories such as minimization or equilibration runs.
Given the large volume of data represented by. xtc files (see above), we could only scratch the surface of the information stored in these trajectory files by analyzing a subset of 779. xtc filesone per dataset in which this type of file was found. We were able to get the size of the molecular systems and the number of frames available in these files (Fig. 3-B ). Of note, the frames do not directly translate to the simulation runtime -more information deposited in other files (e. g. mdp files) is needed to determine the complete runtime of the simulation. The system size was up to more than one million atoms for a simulation of the TonB protein  (Virtanen et al. , 2020). The cumulative distribution of the number of frames showed that half of the files contain more than 10,000 frames. This conformational sampling can be very useful for other research fields besides the MD community that study, for instance, protein flexibility or protein engineering where diverse backbones can be of value.
We found an. xtc file containing more than 5 million frames, where the authors probe the picosecond-nanosecond dynamics of T4 lysozyme and guide the MD simulation with NMR relaxation data  (Kümmerer et al. , 2021). Extending this analysis to all 28,559. xtc files detected would be of great interest for a more holistic view, but this would require an initial step of careful checking and cleaning to be sure that these files are analyzable. These results bring a first explanation on why there is not a single special-purpose repository for MD trajectory files. Databases dedicated to molecular structures such as the Protein Databank   3-C ). Interestingly, the system sizes in. gro files exceeded those of the analyzed. xtc files (Fig. 3-B ). Even if we cannot exclude that the limited number of. xtc files analyzed (779.
xtc files selected from 28,559. xtc files indexed) could explain this discrepancy, an alternate hypothesis is that the size of an. xtc file also depends on the number of frames stored. To reduce the size of. xtc files deposited in data repositories, besides removing some frames, researchers might also remove parts of the system, such as water molecules. As a consequence for reusability, this solvent removal could limit the number of suitable datasets available for researchers interested in re-analysing the simulation with respect to, in this case, water diffusion. While the size of systems extracted from. gro files was homogeneously spread, we observed a clear bump around system sizes of circa 8,500 atoms/particles. This enrichment of data could be explained by the deposition of ∼340. gro files related to the simulation of a peptide translocation through a membrane (Fig.
To do so, comparison with an. xtc file from the same dataset may help to answer this specific question. However, in this study, we were interested in MD setup practices, in particular what simulation time researchers would set up their system with -likely in the mindset to reach that ending time. We restricted this analysis to the 4,623. mdp files that used the md or sd integrator, and that have a simulation time above 1 ns. We found that the majority of the. mdp files were used for simulations of 50 ns or less (see Fig. 4-A ). Further, 697. mdp files with simulations times set-up between 50 ns and 1 µs and 585. mdp files with simulation time above 1 µs were identified. As analyzing. gro files showed a good proportion of coarse-grained models (Fig. 3-B,C ), we discriminated simulations setups for these two types of models using the time step as a simple cutoff.
, 2016). Interestingly, we noticed the appearance of several pikes at 400 K, 600 K, and 800 K, which were not present before the end of the year 2022. These peaks corresponded to the same study related to the stability of hydrated crystals  (Dybeck et al. , 2023). Overall, this analysis revealed that a wide range of temperatures have been explored, starting mostly from 100 K and going up to 800 K. To encourage further analysis of the collected files, we shared our data collection with the community in Zenodo (see Data and code availability section). The data scrapping procedure and data analysis is available on GitHub with a detailed documentation. To let researchers having a quick glance and explore this data collection, we created a prototype web application called MDverse data explorer available at https://mdverse.
streamlit. app/ and illustrated in Fig. 5-A. With this web application, it is easy to use keywords and filters to access interesting datasets for all MD engines, as well as. gro and. mdp files. Furthermore, when available, a description of the found data is provided and searchable for keywords (Fig. 5-A , on the left sidebar). The sets of data found can then be exported as a tab-separated values (. tsv) file for further analysis (Fig. 5-B ).
Towards a better sharing of MD data With this work, we have shown that it was possible to not only retrieve MD data from the generalist data repositories Zenodo, Figshare and OSF, but to shed light onto the dark matter of MD data in terms of learning current scientific practice, extracting valuable topology information, and analysing how the field is developing. Our objective was not to assess the quality of the data but only to show what kind of data was available. The 𝐸𝑥 2 strategy to find files related to MD simulations relied on the fact that many MD software output files with specific file extensions. This strategy could not be applied in research fields where data exhibits non-specific file types. We experienced this limitation while indexing zip archives related to MD simulations, where we were able to decide if a zip archive was pertinent for this work only by accessing the list of files contained in the archive.
This valuable feature is provided by data repositories like Zenodo and Figshare, with some caveats, though. As of March 2023, we managed to index 245,756 files from 1,979 datasets, representing altogether 14 TB of data. This is a fraction of all files stored in data repositories. For instance, as of December 2022, Zenodo hosted about 9. 9 million files for ∼1. 3 PB of data  (Panero and Benito, 2022). All these files are stored on servers available 24/7. This high availability costs human resources, IT infrastructures and energy. Even if MD data represents only 1 % of the total volume of data stored in Zenodo, we believe it is our responsibility, as a community, to develop a better sharing and reuse of MD simulation files -and it will neither have to be particularly cumbersome nor expensive.
Guidelines for better sharing of MD simulation data Without a community-approved methodology for depositing MD simulation files in data repositories, and based on the current experience we described here, we propose a few simple guidelines when sharing MD data to make them more FAIR (Findable, Accessible, Interoperable and Reusable): • Avoid zip or tar archives whose content cannot be properly indexed by data repositories. As much as possible, deposit original data files directly. • Describe the MD dataset with extensive metadata. Provide adequate information along your dataset, such as: -The scope of the study, e. g. investigate conformation dynamics, benchmark force field,. -The method on a basic (e. g. quantum mechanics, all-atom, coarse-grain) or advanced (accelerated, metadynamics, well-tempered) level.
Store this metadata in the description of the dataset. An adaptation of the Minimum Information About a Simulation Experiment (MIASE) guidelines  (Waltemath et al. , 2011)  in the context of MD simulations would be useful to define required metadata. • Link the MD dataset to other associated resources, such as: -The research article (if any) for which these data have been produced. Datasets are usually mentioned in the research articles, but rarely the other way around, since the deposition has to be done prior to publication. However, it is eminently possible to submit a revised version, and providing a link to the related research paper in updated metadata of the MD dataset will ease the reference to the original publication upon data reuse. -The code used to analyze the data, ideally deposited in the repository to guarantee availability, or in a GitHub or GitLab repository.
-Any other datasets that belong to the same study. • Provide sufficient files to reproduce simulations and use a clear naming convention to make explicit links between related files. For instance, for the Gromacs MD engine, trajectory. xtc files could share the same names as structure. gro files (e. g. proteinA. gro & proteinA. xtc). • Revisit your data deposition after paper acceptance and update information if necessary. Zenodo and Figshare provide a DOI for every new version of a dataset as well as a 'master' DOI that always refers to the latest version available. These guidelines are complementary to the reliability and reproducibility checklist for molecular dynamics simulations  (Commun Biol, 2023). Eventually, they could be implemented in machine actionable Data Management Plan (maDMP)  (Miksa et al.
Improving metadata of current MD data While indexing about 2,000 MD datasets, we found that title and description accompanying these datasets were very heterogeneous in terms of quality and quantity and were difficult for machines to process automatically. It was sometimes impossible to find even basic information such as the identity of the molecular system simulated, the temperature or the length of the simulation. Without appropriate metadata, sharing data is pointless, and its reuse is doomed to fail  (Musen, 2022). It is thus important to close the gap between the availability of MD data and its discoverability and description through appropriate metadata. We could gradually improve the metadata by following two strategies. First, since MD engines produce normalized and well-documented files, we could extract parameters of the simulation by parsing specific files.
We already explored this path with Gromacs, by extracting the molecular size and composition from. gro files and the simulation time (with some limitations), thermostat and barostat from. mdp files. We could go even further, by extracting for instance Gromacs version from. log file (if provided) or by identifying the simulated system from its atomic topology stored in. gro files. This strategy can in principle be applied to files produced by other MD engines. A second approach that we are currently exploring uses data mining and named entity recognition (NER) methods  (Perera et al. , 2020)  to automatically identify the molecular system, the temperature, and the simulation length from existing textual metadata (dataset title and description), providing they are of sufficient length. Finally, the possibilities afforded by large language models supplemented by domain-specific tools  (Bran et al.
Future works In the future, it is desirable to go further in terms of analysis and integrate other data repositories, such as Dryad and Dataverse instances (for example Recherche Data Gouv in France). The collaborative platform for source code GitHub could also be of interest. Albeit dedicated to source code and not designed to host large-size binary files, GitHub handles small to medium-size text files like tabular. csv and. tsv data files and has been extensively used to record cases of the Ebola epidemic in 2014  (Perkel, 2016)  and the Covid-19 pandemic (https://github. com/CSSEGISandData/COVID-19). Thus, GitHub could probably host small text-based MD simulation files. For Gromacs, we already found 70,000 parameter. mdp files and 55,000 structure. gro files. Scripts found along these files could also provide valuable insights to understand how a given MD analysis was performed.
Finally, GitHub repositories might also be an entry point to find other datasets by linking to simulation data, such as institutional repositories (see for instance  (Pesce and Lindorff-Larsen, 2023) ). However, one potential point of concern is that repositories like GitHub or GitLab do not make any promises about long-term availability of repositories, in particular ones not under active development. Archiving of these repositories could be achieved in Zenodo (for data-centric repositories) or Software Heritage (Di Cosmo and Zacchiroli, 2017) (for source-code-centric repositories). An obvious next step is the enrichment of metadata with the hope to render open MD data more findable, accessible and ultimately reusable. Possible strategies have already been detailed previously in this paper.
We could also go further by connecting MD data in the research ecosystem. For this, two apparent resources need to be linked to MD datasets: their associated research papers to mine more information and to establish a connection with the scientific context, and their simulated biomolecular systems, which ultimately could cross-reference MD datasets to reference databases such as UniProt  (Consortium, 2022) , the PDB  (Berman et al. , 2000)  or Lipid Maps  (Sud et al. , 2007). For already deposited datasets, the enrichment of metadata can only be achieved via systematic computational approaches, while for future depositions, a clear and uniformly used ontology and dedicated metadata reference file (as it is used by the PLUMED-NEST: Bonomi et al. (.