dataesr/scientific-paragraphs-categorization huggingface dataset:
Timing Step 1, data conversion: 1 h up to a few days (depending on size of dataset and computer setup)  The value of the minimum cluster size can be reduced. The minimum cosine score can also be decreased to increase the number of edges in the networks Molecules known to be structurally similar do not appear to form a cluster Check consensus spectra for the molecules of interest. It is possible that lowabundance noisy spectra are included, which results in poor consensus. Although most ions that are observed can be fragmented, some ions that are fragmented may have few ions to reliably network; these ions should be avoided. We advise using caution when looking at spectra with less than four fragment ions, on the basis of FDR estimates of spectral matching against libraries  100. For some classes of compounds that do not fragment efficiently-for example, certain lipids-the MS  2   Step 53, subscribing to a dataset and living data: 5 min into the future Box 1, sample information (metadata) collation and input: typically 1-2 h for a small dataset; up to a few days for large, complex metadata entries of large datasets.
The current reference libraries used (curated in speclibs, https://gnps. ucsd. edu/ProteoSAFe/libraries. jsp, December 2018) are listed in the supporting information (Supplementary Table  3 ). To illustrate the utility of GNPS in revealing the extent of suites of related NPs, the discovery of new stenothricins-GNPS 1-5 from Streptomyces strains reported in Wang et al. 11  is revisited here. The dataset MSV000083381 comprises MS 2 data for n-butanol and methanol extracts from each of Streptomyces sp. DSM5940 and Streptomyces roseosporus NRRL 15998 cultures grown on solid agar, together with a metadata table that links each of the four MS 2 data files with the originating Streptomyces strain. In reproducing the observation of a distinct subnetwork comprising the MS 2 data from Streptomyces sp.
With a network in hand, there are a number of data analysis tools and experimental validation steps that can be performed. As discussed in Steps 41-43, to legitimize a library annotation beyond inspecting mirror plots, the user should verify the molecular formula and identify associated adducts using MS 1 data. In addition, rationalization based on biological source is recommended. Ideally, an annotation is authenticated by comparison with a known standard compound or isolation and full characterization. In the example followed throughout the protocol, the molecular structures of the new conjugated bile acids from the mouse duodenum were confirmed by comparison with synthetic standards. For more complex structures such as those in the stenothricin example  11  (Fig. 6 ), the most abundant analog, stenothricin-GNPS 2, was purified for acquisition.
6 Click 'submit'. 7 Sign in to your new GNPS account (http://massive. ucsd. edu/ProteoSAFe/). Once login is successful, navigate to the home page by clicking the user workspace banner (Box 3). 8 Check that your GNPS credentials work for logging in to MassIVE. Deposit data files into the MassIVE web server using an FTP client • Timing 30 min Choose FTP client and log in 9 Choose an FTP client. A number of options are available for free dedicated FTP clients; of these, the following are popular choices that have been tested with MassIVE: WinSCP, CoreFTP (http://www. coreftp. com/), and CoffeeCup Free FTP for Windows (https://www. coffeecup. com/free-ftp/), and Cyberduck or FileZilla (https://filezilla-project. org/) for Macintosh. Additional online tutorial materials on how to submit a dataset to MassIVE can be accessed at: https://ccms-ucsd.
github. io/ GNPSDocumentation/datasets/#submitting-gnps-massive-datasets. See also Supplementary Fig. 3. ! CAUTION When downloading an FTP client for use, make sure it comes from a trusted source to avoid malware. Data files transferred to MassIVE should be in. mzXML,. mzML, or. mgf formats. The data that are uploaded should not be in a file archive (e. g. ,. zip,. tar) format. We also recommend that the original vendor raw data files (e. g. ,. wiff for AB SCIEX,. yep for Agilent,. d for Bruker,. lcd for Shimadzu,. raw for Thermo Scientific) be uploaded together with the open formats as described below. 10 Log in to the FTP server with the host name 'massive. ucsd. edu' using your MassIVE web account.
The corresponding online tutorial can be. accessed at https://ccms-ucsd. github. io/GNPSDocumentation/quickstart/. Run the MassIVE dataset submission workflow on the uploaded files 11 Load the home page for MassIVE from the GNPS home page by scrolling down to the GNPS-MassIVE datasets section and clicking on the 'Deposit dataset' bar in the 'Create Public datasets' block. Alternatively, click on the 'Submit your data' link in the paragraph titled 'Submit Data' on the MassIVE home page. A direct way to deposit the data is to navigate directly to the MassIVE home page (http://massive. ucsd. edu/ProteoSAFe/). This will bring up the Dataset Submission workflow input form, on which there are various numbers of fillable fields under each of the sections described in Steps 12-18.
In particular, check whether the first column is named 'filename', whether all filenames match exactly the files uploaded to GNPS and have '. mzXML' extensions (or another compatible file format), whether each metadata column uses the prefix 'ATTRIBUTE_', and that there are no trailing spaces in any of the headings. Step 32, visualize and analyze a network: 1 h to a few days/weeks (depending on size and complexity of network) Steps 33-39, assessing the quality of a library hit : 30 min-days Steps 40-43, propagating annotations through manual interpretation of the networks: days-weeks Steps 44-47, capturing information by adding reference spectra from your data: 10 min-days Steps 48 and 49, data sharing and reproducibility of molecular networking: 1 h-a few hours Step 50, cloning a job: 10 min Steps 51 and 52, accessing an existing dataset on GNPS: 5 min.
Acknowledgements We acknowledge funding from the following:  National Research System (SNI) of SENACYT Panama (C. A. B. P. , M. H. C. , J. L. -B. , and M. G. ); the  Gordon and Betty Moore Foundation  (P. C. D. , N. B. , and K. L. M. ); the  National Institutes of Health  ( GM122016-01 ; K. L. M. ); the  National Science Foundation  ( DEB1354944 ; R. M. T. ) and (IOS-1656481; A. M. C. R and P. C. D). A. K. J. acknowledges an American Society for Mass Spectrometry 2018  Postdoctoral Career Development Award. D. P. was supported through the  Deutsche Forschungsgemeinschaft (DFG ;  PE 2600/1 ). F. T. and N. N. acknowledge  Shimadzu South Africa (Pty) Ltd  for the support and training. We are grateful for grant  R03 CA211211  (P. C. D. ) on reuse of metabolomics data and grant  P41 GM103484  (P.
Flowchart of the protocol, delineating the workflow through Step 32 (Steps 33-53 address optional analyses, visualizations and sharing of data and molecular networks). The workflow comprises tandem mass spectrometry data acquisition, conversion, upload and networking to visualization. Readers following the example step-by-step instructions can follow these steps to generate a publishable network. Int. , intensity. Int. Set the correct instrument parameters m/z for MS 2 acquisition Collect MS 2 spectra Step 1 Use MS convert workflows for agilent, AB Sciex, Shimadzu, Thermo, and Waters instruments or Use Bruker DataAnalysis (Supplementary Methods) Convert data files Steps 2-19 Upload using FTP client Make dataset public Upload data to MassIVE Steps 20-31 Choose files for analysis Set recommended parameters Upload metadata Submit Job to GNPS Steps 32 Visualize in web browser or Visualize in Cytoscape View molecular network Fig.
By requiring more identical spectra to be merged (clustered) before considering the MS 2 spectral alignments, it will ensure that only reproducible and higherquality data are used in the final molecular network. The default is 2, but if it is a very large dataset (hundreds to thousands of files) one can use 5 or more, whereas for smaller datasets (e. g. , 1 or 2 files) it can be set to 1 or 2 Use 4 for example MSV000083437 Run MSCluster Clusters MS 2 spectra and creates consensus MS 2 spectra using the specified mass tolerance settings Set to 'yes' for classic molecular networking Set to 'yes' for example MSV000083437 Maximum Connected Component Size (Beta).
This removes the residual precursor ion, which is frequently observed in MS 2 spectra in the comparison of all spectra for molecular networking Filter Library Applies the above precursor ion window filter Apply this filter, which is the default option to the library as well Filter Peaks in 50 Removes peaks that are not one of the top six Da Window most intense within a ±50 Da window This is commonly turned on. It is dependent on the dataset: if samples contain a large number of low-mass molecules or are complex mixtures containing compounds of low titer, this filtering should be turned off, because it may filter out relevant peaks that could be signals Box 2 | Feature-based molecular networking.
Waters MassLynx MS software (http://www. waters. com/waters/en_US/MassLynx-MS-Software/nav. htm?loca le=en_US&cid=513662) • FTP client (e. g. , WinSCP for Windows (https://winscp. net/eng/download. php); Cyberduck for Macintosh (https://cyberduck. io/download/)) • Web browser (Firefox or Google Chrome to access GNPS) • Cytoscape for data visualization: (https://cytoscape. org/ (current version at the time of writing was 3. 7. 1; current version at time of publication is 3. 7. 2)) • Software relevant to optional pipelines (e. g. , 2D or 3D Visualization; 113 feature-based molecular networking (Box 2)) • OpenMS TOPPView (https://github. com/OpenMS/OpenMS/releases) • AB SCIEX MS Data Converter (Beta 1. 3) is freely available for download from the AB SCIEX website (https://sciex. com/software-support/software-downloads) • AB SCIEX Analyst Software 1.
When downloading ProteoWizard, the version of Windows must be specified and. NET Framework 3. 5 SP1 and 4. 0 must be installed. MSConvert is the recommended software for conversion of data acquired on AB SCIEX, Agilent, Shimadzu, Thermo Scientific, and Waters instruments. (ii)enablesWindows users to convert vendor formats to GNPS-compatible formats (. mzXML,. mzML,. mgf). Refer to the Supplementary Methods and https://ccms-ucsd. github. io/GNPSDocumentation/fileconversion/foradditional online data conversion tutorial information. ?TROUBLESHOOTING NATURE PROTOCOLS PROTOCOL NATURE PROTOCOLS | www. nature. com/nprotDatasubmission to GNPS/MassIVE • Timing 10 min cCRITICAL Create an account with GNPS in order to submit datasets and create workflows, as well as to receive emails about the outcomes (Supplementary Fig.
github. io/ GNPSDocumentation/datasets/#submitting-gnps-massive-datasets. See also Supplementary Fig. 3. ! CAUTION When downloading an FTP client for use, make sure it comes from a trusted source to avoid malware. Data files transferred to MassIVE should be in. mzXML,. mzML, or. mgf formats. The data that are uploaded should not be in a file archive (e. g. ,. zip,. tar) format. We also recommend that the original vendor raw data files (e. g. ,. wiff for AB SCIEX,. yep for Agilent,. d for Bruker,. lcd for Shimadzu,. raw for Thermo Scientific) be uploaded together with the open formats as described below. 10 Log in to the FTP server with the host name 'massive. ucsd. edu' using your MassIVE web account username and password in the FTP client program for FTP file transfer. Most FTP clients use this 'Quick Connect' feature.
The corresponding online tutorial can be. accessed at https://ccms-ucsd. github. io/GNPSDocumentation/quickstart/. Run the MassIVE dataset submission workflow on the uploaded files 11 Load the home page for MassIVE from the GNPS home page by scrolling down to the GNPS-MassIVE datasets section and clicking on the 'Deposit dataset' bar in the 'Create Public datasets' block. Alternatively, click on the 'Submit your data' link in the paragraph titled 'Submit Data' on the MassIVE home page. A direct way to deposit the data is to navigate directly to the MassIVE home page (http://massive. ucsd. edu/ProteoSAFe/). This will bring up the Dataset Submission workflow input form, on which there are various numbers of fillable fields under each of the sections described in Steps 12-18. NATURE PROTOCOLS PROTOCOL NATURE PROTOCOLS | www.
In particular, check whether the first column is named 'filename', whether all filenames match exactly the files uploaded to GNPS and have '. mzXML' extensions (or another compatible file format), whether each metadata column uses the prefix 'ATTRIBUTE_', and that there are no trailing spaces in any of the headings. Step 32, visualize and analyze a network: 1 h to a few days/weeks (depending on size and complexity of network) Steps 33-39, assessing the quality of a library hit : 30 min-days Steps 40-43, propagating annotations through manual interpretation of the networks: days-weeks Steps 44-47, capturing information by adding reference spectra from your data: 10 min-days Steps 48 and 49, data sharing and reproducibility of molecular networking: 1 h-a few hours Step 50, cloning a job: 10 min Steps 51 and 52, accessing an existing dataset on GNPS: 5 min 28 & 31 GNPS job fails because of improper The metadata file must be formatted as a tab-separated.
txt file metadata format 31 Job fails with the message 'spectral library This means that the spectral library search step used too much memory and had search exceeded memory' to be terminated. This is probably caused by changing the set of spectral libraries used in the search (such as removing the spectra filtering). This issue can potentially be resolved by increasing the maximum cluster size value to reduce the number of searched spectra. It is not recommended to change the set of libraries included, unless you are an advanced user. Remove all libraries except for the default 'speclibs' and rerun 32B(ii) Network is too large to view in Cytoscape If a dataset cannot be loaded into Cytoscape, a subnetwork of interest can be opened. Alternatively, larger networks can be opened on a computer with more RAM 32B(xii) Protocol user does not know how to include/ exclude blanks.
(  1 )-(5) and the system evolves according to its dynamics while the feed-forward weights are being updated. Then we reset the neural fields activity to zero. We run each experiment ten times using a different pseudo-random number generator (PRNG) seed each time (the PRNG seeds are given in Appendix 6. 3: the same initial conditions and set of PRNG seeds sequence were used in each experimental condition). The source code is written in Python (Numpy-, Numba-, Sklearn, and Matplotlib-dependent) and are freely distributed under the GPL 3-Clause License (https://github. com/gdetor/som_stability). All the parameters used in numerical simulations are summarized in Table  1. All simulations ran on an Intel NUC machine equipped with an Intel i7-10th generation processor and 32 GB of physical memory, running Ubuntu Linux (20.
New information Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country's biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities.
Introduction Since 2008, iNaturalist (https://www. inaturalist. org/) has been crowdsourcing biodiversity observations made by citizen scientists as well as their taxonomic identifications. Hundreds of publications have already taken into account iNaturalist data for use in research, conservation, and policy (e. g. Ocampo-Peñuela et al. 2016 , Chandler et al. 2017 , Heberling and Isaac 2018). There are three key themes that iNaturalist embraces: social interaction; shareability of data, tools, and code; and scalability of the platform and community (Seltzer 2019). The advent of large, technology-based resources allows ecologists and biologists to work at spatio-temporal scales previously unimaginable  (White et al. 2015). With 50M observations accompanied by photo or audio evidence, the global iNaturalist dataset is one of the largest online collections of biodiversity data.
It is partially represented in the GBIF, with the exclusion of observations which remain unidentified or have unconfirmed or missing license information. Nonetheless, the GBIF export tools provide excellent data usability, and the resulting exports come with a DOI one can use for citation in publications. The GBIF data usage counter shows that iNaturalist GBIF-mediated data gained 781 citations (as of 11 Sep 2020) making it one of the most commonly used datasets among the GBIF (Ueda 2020). Many research papers focus on the employment of iNaturalist data as a primary source  (Heberling and Isaac 2018, Seregin et al. 2020). For instance, iNaturalist includes dozens of metadata fields for every observation and was employed as a case study in the theory of long-tailed datasets  (Cui et al. 2019).
2019 ). In addition, there are examples of iNaturalist usage during university courses of classical zoology and botany together with standard field guides and keys  (Unger et al. 2020). The iNaturalist dataset at various taxonomic and/or geographical extents has been checked for completeness of data against complete literature data (Goldstein et al. 2018), expert-based range maps (Fourcade 2016), museum collections  (Spear et al. 2017) , and available inventories within protected areas (Jacobs and Zipf 2017). Vahidi et al. (2017)  performed a general quality assessment of iNaturalist data which made possible the revealing of the majority of attribute and positional errors among the crowdsourced biodiversity observations. Borzée et al. (2019)  published a case study on cross-verification of iNaturalist observations against published georeferenced molecular data, whereas Maritz and Maritz (2020) compared Facebook versus iNaturalist as data sources in the assessment of trophic interactions.
Prudic et al. (2018)  verified the completeness of iNaturalist data with various field techniques of butterfly data collection. The project "Flora of Russia", which includes all verified ("research-grade") observations of vascular plants from the country, was launched by the Moscow University team on Jan. 9, 2019 to support data collection for the "Atlas of the Russian flora"  (Seregin et al. 2020). During the first 20 months the number of identified and verified iNaturalist observations of vascular plants from Russia increased 68-fold and the number of involved users increased 10-fold. Here we present the characteristics of the dataset as for Sep. 9, 2020, soon after the project reached two notable milestones of 750,000 verified observations and 10,000 observers (Fig. 1 , Fig. 2 , Fig. 3 ).
General description Purpose: For a number of years Russian professional and amateur biologists were using Internetbased national networking systems of the georeferenced data collection for birds, invertebrates, and plants. For instance, Plantarium is the most popular Russian-language resource for collecting plant and lichen photographs from around the world with emphasis on Russia and adjacent regions. However, unlike iNaturalist, it does not allow data export nor is this data included in the GBIF, since photos and other data lack license indications. Also, contributing observations to Plantarium requires more effort from the members. After digitisation of the nation's second largest herbarium (Seregin 2018), the Moscow University team launched a public awareness campaign to support community-generated data collection for plants.
Russia on iNaturalist: By late 2018, Russia was the 18th country on iNaturalist in terms of the number of verifiable observations  (47, 888). After 20 months of the project activity, we can see drastic changes in the biodiversity data coverage across Russia with a strong emphasis on tracheophytes. Currently, Russia holds the fifth place among countries represented on iNaturalist in terms of the number of verifiable observations of all groups of organisms and the third place by observations of vascular plants in particular (Table  1 ). Among the top ten countries, Russia has the highest proportion of tracheophyte observations of all uploaded to iNaturalist (62. 2%). A community of birdwatchers is also quite active as compared to other top countries, whereas other groups of organisms are still lacking much attention (Table  2 ).
Birds are the primary object of attention for at least eight co-authors of this paper, whereas three of us are focused on fungi. Russia has the highest proportion of vascular plants among identified and confirmed observations which are classified as "research grade" on iNaturalist (Table  3 ). Moreover, Russia is the leading country on iNaturalist among the top ten with regard to the proportion of confirmed observations among all tracheophyte records. As we showed in 2019  (Seregin et al. 2020) , the number of unconfirmed plant observations in Russia usually rapidly increases from May to August and decreases from September to April, when experts most intensively work with the backlog of unprocessed observations. To facilitate further accumulation of the project's data into the GBIF we ask our observers to specify open Creative Commons licenses such as CC0 (http://creativecommons.
org/ publicdomain/zero/1. 0/), 2) CC-BY (http://creativecommons. org/licenses/by/4. 0/), 3) CC-BY-NC (http://creativecommons. org/licenses/by-nc/4. 0/) for their observations. We do this on a regular basis in the form of the project's journal posts available to every member of our community. As a result of this activity, 83. 8% of all observations on iNaturalist from Russia (and as many as 85. 3% in tracheophytes) are freely licensed making Russia the leader in open-access biodiversity data on iNaturalist (Table  4 ). As a result of intense expert activity and the promotion of free licensing, 73. 8% of tracheophyte records from Russia have become available in the GBIF (Table  5 ) which is the highest proportion among the leading countries in iNaturalist. Since 2020, the iNaturalist dataset has become the largest source of data on the Russian biodiversity available through the GBIF.
The amount of observers with at least a single verifiable observation is not so high in Russia, equaling just 14K (Table  6 ). Nonetheless, the average productivity of the members of the community is extremely high. On average 93 verifiable observations have been created by each observer across all groups of organisms, while with regard to vascular plants the number is 73 "research-grade" observations per observer, which makes the highest level of observer activity among the top ten countries on iNaturalist. Russia is globally unique taking into account the active growth of data within the "Flora of Russia" project. Among the top ten countries on iNaturalist Russia has achieved: 1. the highest proportion of tracheophytes among all observations; 2. the highest proportion of identified and verified ("research-grade") observations among tracheophytes; 3.
As of Sep. 5, 2020, the iNaturalist database contained 48. 6M observations that have met the minimum quality requirements (Table  1 ), of which 29. 2M have achieved "research-grade" (Table  3 ). Unfortunately, only 19. 7M observations were exported to the GBIF due to copyright restrictions (Table  5 ). The implementation of artificial intelligence (AI) for identification is a key feature of iNaturalist  (Van Horn et al. 2018 , Cui et al. 2018). It gives the users a suggestion about the most similar species after analysing the photos ("visually similar") and taking into account the geographical distribution of other records ("seen nearby"). Initially, the portal's AI compared the newly uploaded photos with a basic set of images, which in 2017 comprised 859,000 photos of more than 5,000 species.