dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2. 2. c. Order Copepoda (copepods) (Figures 5, 6C Calanus pacificus was prevalent and widespread in many samples (Figure  6C ), whose morphological proportions and sequence abundances appeared similarly correlated for LrCOI and Cop16S (Figure  7 ). LrCOI read proportions and morphological prevalence also corresponded for Centropages abdominalis (Figure  7A ), which was widespread in the spring (Figure  6B ). Metridia pacifica was discerned by both LrCOI sequences and morphology in seven cases, and by sequences alone in two others. Figure  7A  shows its significant relationship between LrCOI sequence read proportions versus carbon biomass. Pseudocalanus newmani also was prevalent, whose sequence reads and morphology matched in most samples (Figure  6C , Figure  7A ). In these examples, greater correspondence between the two data sets occurred when the species was abundant (Figure  7 ).
Fish species were best resolved by the combination of the MiFish12S and the FishCytb markers, since the taxa identified by each did not entirely overlap. FishCytb was especially effective at identifying Pleuronectidae flatfish species, some of which were not discerned by MiFish12S (e. g. , English sole Parophrys vetulus, starry flounder Platichthys stellatus, butter sole Isopsetta isolepis, Pacific rock sole Lepidopsetta bilineata, and Pacific sand sole Psettichthys melanostictus), despite their prevalence and sequence abundances. Future analyses for all markers are anticipated to improve in resolution as more species are sequenced and referenced in NCBI [84] and other data repositories.
Species detected by morphology and missed by metabarcoding A few taxa were abundant in our morphology dataset but were not identified with metabarcoding, often due to poor representation in the NCBI reference database  [84]. Notably, cyclopoid copepods were missed by metabarcoding, including the abundant species Ditrichocorycaeus anglicus (up to 15. 3% in biomass). This species was well-represented in NCBI, and the LrCOI forward primer contained just one mismatch, but was not detected with any of the metabarcoding markers; this discrepancy is being further investigated in follow-up work. Cyclopoid copepods in the genus Oithona also were abundant in the morphology counts (up to 9. 43% of the individuals), leading to the expectation that they should be detectable with metabarcoding. However, their small individual sizes led to their proportionally small biomass representation (average 0.
(2019)  [108] concluded positive yet weak correlation between sequence reads and numbers of individuals per species across metabarcoding studies, which averaged R=0. 52±0. 34. Their range [108] corresponded to the correlation values we obtained for copepods. We similarly found that our results were quite variable among different taxa using LrCOI, for which three crustaceans (of 12) had significant corrected p-values (Calanus pacificus, Metridia pacifica, and Euphausia pacifica). Lamb et al. (2019)  [108] discerned that weaker relationships occurred when species varied greatly in body sizes. In our results, correlation between relative abundance and biomass increased when analyses were restricted tojust the Copepoda as opposed to using the entire dataset, suggesting that including taxa having vastly different biomass can skew the results.
Non-indigenous species detections The present investigation identified possible nonindigenous species, whose larval distributions can be assessed with metabarcoding. We evaluated the quality of the taxonomic assignment to each of these species (Methods and S Table  6 ), since invasions can have implications for management responses  [9, 92]. We assessed nine putative nonindigenous marine/estuarine species, four regarded to have high confidence due to percent identity matches to the sequence reference database and our other criteria (S Table  6 ). The remainder are excluded from discussion here due to lower percent identity matches and/or missing reference sequences for potential closely-related local species (see S Table  6 ). Notably, high confidence values support two nonindigenous polychaetes found by the LrCOI marker: Proceraea okadai and Hediste diadroma, which both have known introductions to the Salish Sea (and San Francisco Bay) from Asia  [97] [98] [99].
Species identifications and reference libraries Correct species identifications are vitally important for valid ecological comparisons, yet often are overlooked. Notably,  Bianchi and Gonçalves (2021)  [137] pointed out that misidentifications and problematic taxonomic metadata are a recurrent issue significantly affecting sequence reference databases, including Global Biodiversity Information Facility (GBIF), the Barcode of Life Data System (BOLD), and NCBI GenBank. Misspellings and invalid names sometimes exceed 10% of deposited sequences for a given species, along with poor-quality sequences  [137]. Such mistakes compromise the integrity of reference databases and appear to affect many studies [see  9, 43, 51, 111, [137] [138] [139] [140] [141] [142] [143] [144] ]. Here we found limitations due to the numbers of species that lacked diagnostic sequence data in reference databases at the time of our study (see Figs  6A  and F ).
Around the world, there is growing effort to combat this lack of reference sequences with the increasing cost-effectiveness of whole mitogenome and whole genome sequencing efforts. Such efforts include a collaboration being undertaken for all U. S. marine fishes by NOAA Fisheries, Smithsonian's National Museum of Natural History, and other federal partners (e. g. ,  Hoban et al. 2022 [139,140] ), for zooplankton with the MetaZooGene collaboration by the Scientific Committee on Oceanic Research of the International Science Council [141], and for all marine species with a US Oceans Biocode  (Meyer et al. 2021[142] ). Reference sequence data that are accompanied by specimens, their collection data, and preserved tissue samples in museum repositories are especially valuable [see 137-144]. In the future, metabarcoding data sets such as ours can be used to identify species from the original sequences that were not diagnosable at the time, by querying improved and augmented reference sequence databases.
Ecological patterns and trends Species that are congruently identified by more than one metabarcoding marker and gene region provide strong verification of their presence in field-collected samples  [42, 51]. For rare and poorly known taxa, especially at their early life stages, metabarcoding identifications are very valuableprovided that the sequence reference databases exist and are correct  [137] [138] [139] [140] [141] [142]. The larval macroinvertebrates and fishes assayed in this project are the key to adult recruitment for the abundant fisheries of the Salish Sea and much of the NE Pacific coast. It is these early life history stages that are under the most intense selection, and are the most ephemeral and vulnerable (see  [145] ). For example, it is hypothesized that warming and other anthropogenic factors may alter the timing of phytoplankton blooms, and their reliant zooplankton populations; this may put larval communities out of synchrony with their prey  [12, 13, 16].
Conclusions This multi-gene metabarcoding marker approach holds considerable promise to advance scientific ability for assessing species identities and compositions of planktonic communities, as well as evaluating their responses to environmental conditions. Moreover, the approach is powerful and data-rich, yielding results that are readily integrated into existing ecological sampling and modeling efforts. Currently, there are species identification issues stemming from lack of available reference sequences, along with museum voucher specimens, preserved tissues, and DNA samples for comparisons; sequence databases also are fraught with incorrect erroneous scientific names and incorrect systematic classifications  [9, 43, 51, 111, 135, [137] [138] [139] [140] [141] [142] [143] [144]. Additionally, requiring multiple markers for assessment significantly increases the cost of such analysis.
Acknowledgments: We extend thanks and great appreciation to  Russell Callender  and the  Washington Sea Grant Program  team for their understanding and help, especially during project delays and personnel issues during the Covid19 pandemic. We greatly appreciate collection help from  Jan Newton ,  Terrie Klinger ,  Amanda Winans ,  Amy Wyeth ,  Anna McLaskey , and  Beth Curry  and the  Washington Ocean Acidification program  and research cruises. Morphological identifications were conducted by  BethElLee Herrmann ,  Olga Kalata , and  Amanda Winans  in the laboratory of JEK. Some genetics laboratory work variously was aided by members of the  Genetics and Genomics Group at NOAA Pacific Marine Environmental Laboratory  under the scientific direction of CAS, including  Anna E. Elz ,  Kimberly Andrews ,  Matthew R.
Introduction During the 2017 ''Grand Finale'' stage of the Cassini mission, the spacecraft spent five months in a series of orbits with close-approach periapse distances between Saturn's rings and its upper atmosphere, culminating in the orbiter's planned final plunge into Saturn's atmosphere. The spacecraft skimmed Saturn's equatorial upper atmosphere  et al. , 2006; Cui et al. , 2009; Magee et al. , 2009). Grand Finale data from the Radio and Plasma Wave Science/Langmuir Probe (RPWS/LP) instrument, Magnetospheric Imaging Instrument (MIMI), and Cosmic Dust Analyzer (CDA) also provided complementary in situ measurements of electron and ion densities, dust properties, and other key pieces of information about the local atmosphere and its interaction with the rings and magnetosphere (e. g. ,  Mitchell et al.
, 2018; Hadid et al. , 2019) , which can be resupplied with particles from the C ring  (Waite et al. , 2018; Yelle et al. , 2018; Perry et al. , 2018; Miller et al. , 2020). If the extremely large mass influx rate inferred from the INMS data (> 10 4 kg s -1 into a low-latitude band;  Waite et al. , 2018)  were maintained over time, the rings would be completely eroded within ∼50 Myr (or ∼5000 years for the D ring alone;  Koskinen et al. , 2022). Several questions then arise. Is the current mass influx rate inferred from the INMS in situ measurements anomalously large and caused by some recent, transient disruptive event within the rings, or does it represent a more-or-less steady-state influx that has been operating in the equatorial region over larger time scales? If a disruptive event is the culprit, how recently did it happen? Conversely, if this ringerosion process has been operating over long time scales, what are the consequences for Saturn's atmospheric chemistry, structure, and bulk elemental abundances, as well as for the evolution of the rings? Can any other atmospheric observations be used to help evaluate the possible long-or short-term nature of this enormous ring influx rate seen by INMS? Some of the consequences of this infalling ring-derived vapor and dust on Saturn's ionosphere have been discussed previously.
The forward-modeling procedure is iterated until the H 2 density at closest approach from the INMS measurements is reproduced to within some average specified percent (e. g. , < 5%). Fig. 1  illustrates the resulting pressure-temperature profiles and the H 2 density profiles as a function of ''equivalent radius'' in comparison with the INMS data for orbits 290 and 293. The densities of H 2 and He from orbits 288-292 agree to within tens of percent  (Yelle et al. , 2018) , and orbit 290 is simply used as a representative for the closestapproach -5. 5 • latitude region here. Cassini spacecraft trajectories were such that the measurement latitudes were constantly changing along with the spacecraft's radial position. As mentioned previously, because of Saturn's oblate shape and strong variation in gravity with latitude, the radial distance at which a specific H 2 density would be found at one latitude differs from that at another latitude.
The 1-bar radii are derived from Eq. (8a) of  Strobel et al. (2019). The above expression assumes that the atmospheric temperature and mean molecular mass are constant with radius over the limited radial range that is being used to determine the exospheric temperature from the INMS data -an assumption that is not strictly valid but that provides a reasonable structure model for our purposes. An alternative method would be to convert the vertical coordinate to gravitational potential  (Yelle et al. , 2018; Chadney et al. , 2022; Serigano et al. , 2022). As can be seen, our background structure models have exospheric temperatures within a few degrees of previous analyses  (Yelle et al. , 2018; Waite et al. , 2018; Chadney et al. , 2022)  and consider a thermal structure in the lower-thermospheric ''gap'' region that connects the CIRS limb thermal structure appropriately to the H 2 densities measured by INMS.
Acknowledgments We gratefully acknowledge support from the  NASA   Solar System Workings program ,  United States  grants  80NSSC20K0462  (J. M. ) and  80NSSC19K0546  (L. M. ), and  NASA   Cassini Data Analysis Program ,  United States  grants  80NSSC19K0902  (T. K. , Z. B. , J. M. ) and 80NSSC19K0903 (J. S. ). L. N. F. is supported by a  European Research Council   Consolidator Grant ,  European Union  (under the European Union's  Horizon 2020 research and innovation programme , grant agreement number  723890 ) at the  University of Leicester. J. H. W. is supported by  SwRI , United States internal funds.
GitHub hosts Git repositories and provides issues-tracking services to provide a better collaboration environment for software developers. Issues and Pull-Requests are frequently used in GitHub to discuss and review the software requirements (new features, bugs, etc. ) and software solutions (source code, test cases, etc. ) respectively. The links between Issues and their corresponding Pull-Requests comprise valuable information to keep tracking current development as well as documenting knowledge for future development. Considering a large number of links, such information can be used to train machine learning models for several purposes such as feature location, bug prediction and localization, recommendation systems and documentation generation. To the best of our knowledge, no dataset has been proposed as a ground-truth of links between Issues and Pull-Requests.
In this paper, we propose, PI-Link, a new significant and reliable ground-truth dataset composed of 50369 links that explicitly connect 34732 Issues with 50369 Pull-Requests. These links are automatically extracted from all (907,139) Android projects in GitHub created between January 1, 2011 and January 1, 2021. To better organize and store the collected data, we propose a metamodel based on the concepts of Issues and Pull Requests. Moreover, we analyze the relationships between Issues and their linked Pull Requests based on four features related to their titles, bodies, labels and comments. The selected features are analyzed in terms of their lengths and similarities based on three lexical and one semantic similarity metrics. The results showed promising similarities between Issues and their linked PRs at the lexical and semantic levels.
I. INTRODUCTION GitHub is a hosting platform for software development and version control that supports the concept of collaborative social coding. It provides a friendly environment for contributors to communicate, collaborate, and promote software development. Contributors can easily track bugs, request new features, manage tasks, and handle continuous integration. Issues and Pull-Request (PR) are common software artefacts that are used throughout all software development lifecycles in GitHub. The Issue lets contributors track the development work, and it is managed by the GitHub issuetracking system. The PRs lets other contributors review and discuss the changes before they are submitted. Issues and PRs contain much information about software development and evolution, but from different perspectives: Issues capture the software requirements (e.
Considering a large number of links, such information can be used to train machine learning models for several purposes such as feature location, bug prediction and localization, recommendation systems and documentation generation. To the best of our knowledge, no dataset has been proposed as a ground-truth of links between Issues and Pull-Requests. In this paper, we propose, PI-Link, a new significant and reliable ground-truth dataset composed of 50369 PR-Issue links extracted from 5742 Android projects on GitHub. To do so, we develop a GitHub scraper tool that automatically extracts all explicit PR-Issue links (genuine and examined) from all (907,139) Android GitHub projects created between 2011 and 2021. Moreover, we propose a metamodel to better organize and store the collected PR-Issue links that contain valuable detailed information about Issues and PRs of each link.
This ground-truth dataset can be exploited to evaluate the effectiveness of different future works on this subjects. For example, recovering the missing Issue-PR links, build recommender systems, etc. To make our dataset accessible for the community, we publish it on Kaggle 1   2  : a well-known dataset repository. To better understand the content of our dataset, we analyze the relationships between Issues and their linked PRs based on four features related to their titles, bodies, labels and comments. The selected features are analyzed in terms of their lengths (i. e. , number of tokens) and similarities. For the similarity analysis, we rely on four similarity metrics that are frequently used in NLP. These are Jaccard, Cosine, and Levenshtine as lexical similarity metrics and BERT as a semantic similarity metric.
The results show that some feature similarities are sensitive to the text length, whereas other feature similarities are sensitive to the term frequency. The rest of the paper is structured as follows. Section II introduces the principle of GitHub flow and PR-Issue link. Section III presents the approach to extract our ground-truth dataset. Section IV illustrates our ground-truth metamodel. The analysis of our dataset is presented in Section V. The feature selection and the analysis results are introduced in Section VI and Section 13 respectively. Threats to validity are presented in Section VIII respectively. The use cases of our dataset are introduced in Section IX. Section X discusses the most closely related work. Finally, the conclusion and future works are introduced in Section XI.
II. THE ANATOMY OF GITHUB A. GITHUB FLOW GitHub is a software development and version control hosting repository based on the Git version control system  [13]. In addition to the distributed version control system provided by Git, GitHub provides other features like access control, bug tracking, task management, and continuous integration. GitHub is commonly used to host open-source software development projects, with over 100 million developers working on over 352 million repositories (more than 41 million public repositories  [14] ) by November 2022  [15]   [16]. GitHub flow is a distributed branch-based workflow designed to work with Git and GitHub. The GitHub flow is helpful for software development teams to manage each phase of software development easily and make deployments regularly.
B. PR-ISSUE LINKS IN GITHUB Issues and PRs are software artefacts that are not supported by Git but by GitHub. On the one hand, the primary purpose of using Issues is to track development work on GitHub, which is usually stored in issue-tracking systems. It can be used to track new ideas, bugs, tasks, and other important information. On the other hand, PRs are used to propose and collaborate on changes with other reviewers and contributors, which is usually stored in version control systems. Issue and PR contain much information about software evolution, but from different perspectives: Issues describe the problem domain (e. g. requirements and bugs), while PRs reflect the solution domain (developing requirements, fixing bugs). The links between PRs and issues are important and valuable because they link the problems with their solutions.
III. DATASET EXTRACTION APPROACH To automatically collect our dataset, we developed a GitHub scraper tool named PR-Issue-Scraper using python. It is published in GitHub 5. PR-Issue-Scraper extracts explicit PR-Issue links using GitHub GraphQL API  [22]. We design our tool to deal with the GraphQL request limitation; hence GitHub GraphQL API rate limit is 5,000 points per hour  [23]. We ran PR-Issue-Scraper to target all Android repositories on GitHub in the time period between the 1st of January 2011 to the 1st of January 2021. The run takes around 30 days (24/7) to archive our target. The architecture of PR-Issue-Scraper is composed of three main components: Scraper, Request-meter and Validator, as presented in Figure  4. The Scraper component is responsible for managing queries requested from GitHub to collect data using GraphQL APIs.
The Request-meter component controls the Scraper component to be within the request limit range (i. e. , 5,000 points 6  per hour). The Validator component aims to verify the results of the Scraper and remove noise data, e. g. Issue-Issue links, PR-PR links. For better understanding, Algorithm 1 illustrates the detailed process of PR-Issue-Scraper. In the beginning, all Android repositories placed on GitHub were queried using GitHub GraphQL API. To retrieve only Android projects, we assume that an Android project should have the "Android" keyword in its description and should be implemented using Java or Kotlin programming languages. However, many non-Android projects could have previous assumptions. Therefore, all repositories returned from the query are filtered to eliminate the non-Android projects.
To do so, all repositories are investigated to check whether they have an AndroidManifest. xml file or not. This comes from the fact that any android project contains an AndroidManifest. xml file. After that, for   each Android project, the closed Issues are queried. We targeted closed Issues to guarantee that they are solved and could be linked with a PR. As open Issues do not have links to PRs, we exclude them from our dataset. Next, all closed Issues are inspected to check their explicit links with other software artefacts. The explicit links can be found in the Issue sidebars (c. f. Figure  3 ). We collect data from Issue sidebars using the timelineitems interface provided by GraphQL APIs. The returned explicit links connect the current Issue with PRs, Issues or merge requests. As we aim to collect PR-Issue links, we exclude the other ones.
As a result, only Issues that have PR-Issue links are returned in our dataset. To deal with the rate limit of the GraphQL APIs, each query is examined before it is submitted to the GraphQL API using the RateLimitMeeterListener function. RateLim-itMeeterListener verifies whether the current query exceeds the limit rate or not. In case it exceeds, RateLimitMeeterListener divides the query to make it fit with the rate limit.
B. ISSUE Issues are used to track ideas, tasks, bugs or feedback on GitHub. Issues tackle challenges in the requirements/specifications, workflow, design, implementation, or even production in software development. Each Issue has a unique number based on its repository. This unique number could be used as a reference to its Issue (e. g. could be used to establish an implicit link by a PR). The issue url is a webbased url. The title and body are textual data to describe the Issue. The creation and closing Issue time-stamps are Algorithm 1: PR-Issue-Scraper Algorithm.