dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials All Studied sequences are available in GenBank under accession numbers as follows: UENI00000000. 1, UEND00000000. 1, UENF00000000. 1, UENG00000000. 1, UICY00000000. 1, UENA00000000. 1, UENB00000000. 1, AP014880. 1, ANFT00000000. 1, CP020433. 2, UENJ00000000. 1, NZ_UZBH00000000. 1, UENK00000000. 1, UENH00000000. 1, CP003857. 1, CP003858. 1, UENC00000000. 1, JUZI00000000. 1, CP012718. 1, AFXO00000000. 1, UENE00000000. 1, PNRP00000000. 1, PNRI00000000. 1, PNRH00000000. 1, ATFK00000000. 1, AP010969. 1, AJKN00000000. 1.
Other small changes including adding a 9-codon N-terminal sequence ablated the phenotype and reversed it to no death, despite the additional sequence being smaller than many common gene tags that typically do not interfere with function. This small change also alters predicted RNA structure (Figure  2 figure supplement 1). We were unable to assess protein levels to determine translational differences due to lack of an antibody. However, the data demonstrates that the wmk transgene is functionally sensitive to some post-transcriptional changes, at least in the N-terminal region. What, then, do these results illuminate about transgenic wmk function and the utility of transgenic research in light of the potential for marked influences of synonymous changes on phenotype? First, as discussed, the wmk transgenic phenotypes are likely sensitive to post-transcriptional processes.
All statistical analyses for sex ratios and were performed using GraphPad Prism eight software. For sex ratios, a non-parametric Kruskal-Wallis one-way ANOVA followed by Dunn's multiple comparisons test was applied to all gene-expressing categories, followed by the same test but on all nonexpressing categories. For gene expression, groups were compared using the same Kruskal-Wallis one-way ANOVA with Dunn's multiple comparisons test. Significant results are indicated with * symbols in figures, with accompanying p values in captions. Any comparisons with no symbol are nonsignificant. Full statistical information and outputs for all sex ratio and qPCR data is available in the Source Data file.
Acknowledgements This work was supported by  National Institutes of Health (NIH)  grant  R21 AI133522  to SRB, the  Vanderbilt Microbiome Innovation Center , and  NIH  grants  F31 AI143152 ,  K-INBRE P20 GM103418 , and  National Science Foundation (NSF)   Postdoctoral Research Fellowship  ( PRFB )  DBI 2109772  to JIP. We would also like to thank  RL Unckless  for helpful comments on phylogenies.
Input velocities The Ocean Lagrangian Trajectory product (OLTraj) was computed using reprocessed global daily multi-mission altimeter-based surface geostrophic velocities obtained from the Copernicus Marine Services archive from January 1998 to December 2019, gridded at 0. 25° × 0. 25° resolution (Copernicus). This period was selected to overlap with the years of the available satellite ocean-colour observations. Calculation of trajectories OLTraj was generated using the LAgrangian Manifolds and Trajectories Analyser (LAMTA), originally developed by d  'Ovidio et al. (2004)  and subsequently described in  van Sebille et al. (2018). See section Software availability. The LAMTA code has been previously applied in support of in-situ Lagrangian experiments  (d'Ovidio et al. , 2015; Nencioli et al.
Dataset validation Advantages OLTraj was created to encourage, when needed, non-expert users to interpret in-situ and satellite observations by taking into account that surface water masses move. OLTraj provides pre-computed Lagrangian trajectories, which allow non-experts to skip this computing step and focus on analysing their data in a Lagrangian framework. Furthermore, we provide Python examples to demonstrate how to use OLTraj in practical and common applications. A final but important advantage of OLTraj is that it is based on satellite-derived geostrophic velocities. Thus, OLTraj has better spatio-temporal coherence with other satellite products, such as ocean colour and sea-surface temperature, than modelled velocities (see example in Figure  6 ).
Limitations We do not intend to promote OLTraj as the ultimate tool for Lagrangian analysis. Many other state-of-the-art methods are available  (van Sebille et al. , 2018). Instead, the focus of OLTraj is to broaden the application of Lagrangian analysis to non-physical oceanographers. As such, OLTraj has several limitations. One of the main limitations is inherent in its nature. Being a set of pre-computed trajectories, OLTraj has pre-defined spatial resolution (1/8°) and temporal extent (±29 days). These spatial and temporal characteristics were defined based on a trade off between the number and length of the trajectories computed and the size of the dataset produced that consists of daily files spanning over 22 years (the current overall size is approximately 6. 3 Tb). To reduce the burden of storing the entire dataset in a local machine, we have exploited the UK CEDA THREDDS server, which allows users to subset the data before downloading them.
Access via the THREDDS server is however necessarily slower than reading files stored on a local machine. We therefore encourage the users to assess if local or THREDDS access is needed for any specific analysis. The overall length of the dataset (1998-2019) was decided based on the extents of i) the satellite ocean-colour record and ii) of the reprocessed gridded geostrophic velocity product when the OLTraj product was computed. The pre-defined length of the OLTraj product (±29 days) could potentially be seen as another limitation. Yet, methods could be devised to combine multiple OLTraj files and estimate longer Lagrangian trajectories. OLTraj trajectories are as good as the velocities from which they are derived. Although accurate in the open ocean, the gridded geostrophic velocities used to compute OLTraj have limitations near the coast (i.
Alice Della Penna University of Auckland, Auckland, New Zealand This Data Note provides good information for an exciting dataset that is aimed at encouraging non-specialists to work with Lagrangian re-analyses of satellite altimetry. I think tools like this are very valuable for interdisciplinary research and I also think this could be a good tool for teachingexposing students to these approaches early in their studies. Overall, this is a very positive review. I only have a few small comments that I think could be helpful. Most of the potential users of this dataset are likely to have limited experience in Python so I think it's great that Python notebooks for the examples are made available. My suggestion is to include the links to each notebook individually in the text and not only at the end of the manuscript (and in the heading paragraph of the examples section) so that a reader can potentially look at them in parallel while reading the text.
Lake water level monitoring The lake level at Aru Co and Memar Co has been monitored since September 2016 using HOBO (U20-001-01) or Solinst water level loggers, which were installed in the littoral zone of the lake. Because water levels were recorded as changes in pressure (less than 0. 5 cm water level equivalent), air pressure data were subtracted from the level loggers to obtain pressure changes related to water column variations. Daily lake level changes between October 2016 and September 2019 were used in this study at Aru Co. At Memar Co, the lake level is only available from October 2017 to September 2019 due to the loss of the logger in the first year. Water depth of the loggers was measured during fieldwork to calibrate the logger data.
Satellite observation Multiple sources of satellite data, including Landsat images, ICESat and CryoSat-2 satellite altimetry, were explored to detect long-term changes in lake extent and water level. Landsat images downloaded from the USGS website (http: https://doi. org/10. 5194/tc-15-199-2021 The Cryosphere, 15, 199-214, 2021 //glovis. usgs. gov, last access: July 2019) were used to investigate changes in lake area since the 1970s. A total of 30 satellite images between September and November, 1972 to 2018, were selected. Before 1990 Before , only two images (1972 Before and 1976) )  were available. After 1990, almost annual changes in lake area  (no data in 1991, 1995 and 1998)  were extracted. Lake boundaries were extracted in false-color image by manual delineation using ArcGIS 10. 1 software.
The accuracy of manual digitization is controlled within an error of 1 pixel (30 m). ICESat and CryoSat-2 satellite altimetry data were used to detect lake level changes between 2003 and 2017. ICESat altimetry data were processed after  Li et al. (2014)  and were used to examine water level variations between 2003 and 2009. CryoSat-2 data were processed after  Xue et al. (2018)  and were used to investigate water level variations between 2010 and 2018. Both lakes were observed by the ICESat satellite two or three times a year  (Phan et al. , 2012)  and by the CryoSat-2 satellite every 2 or 3 months  (Kleinherenbrink et al. , 2015; Jiang et al. , 2017). Notably, the two datasets are referenced to different ellipsoids and geoid heights. The ICE-Sat data contain corrected surface ellipsoidal heights referenced to the TOPEX/Poseidon ellipsoid and a geoid height referenced to the Earth Gravitational Model (EGM) 2008, while the CryoSat-2 data are referenced to WGS84 and EGM96  (Song et al.
, 2015). At Aru Co, the lowest lake level in May is very stable from year to year as it is controlled by the elevation of the outlet. The ICESat-derived and CryoSat-2-derived lake surface elevations of Aru Co were averaged to be 4936. 67 m a. s. l. in April (n = 2) during the period  2003-2009 and 4937. 04 m a. s. l. in May (n = 5) during the period 2011-2016, respectively. The elevation difference of 0. 37 m was determined as the bias of the two datasets and used to correct satellite altimetry data. Dynamics of the two ice avalanche deposits were investigated through different kinds of satellite images (Sentinel-2, Gaofen-2, Landsat 8 OLI, SPOT-7, Pléiades and ASTER DEMs). A Sentinel-2 satellite image on 21 July 2016 (4 d after the Aru-1 glacier collapse) was acquired to detect the extent of the intruding ice into Aru Co.
In this study, the uncertainty in changes in lake level was estimated to be ±0. 6 m in the 1970s and ±0. 3 m since the 1990s. The relationship between lake area and the reconstructed lake levels was developed using a second-order polynomial-regression model. Continual lake level changes at Memar Co since 1972 were reconstructed using this relationship and the corresponding lake area. 3. 5 Lake surface temperature derived from MODIS satellite data MODIS 8 d land surface temperature products (i. e. , Terra MOD11A2 and Aqua MYD11A2) were used to investigate changes in lake surface temperature at Aru Co and Memar Co. On both platforms (Terra and Aqua), two instantaneous observations were collected every day (Terra -approximately 10:30 and 22:30 local time, Aqua -approximately 13:30 and 01:30 local time).
The MODIS 8 d data are the averaged lake surface temperature of the daily MODIS product over 8 d. We used the latest version of MODIS products (V006) in this study. Only nighttime data were used because there was less cloud cover at night  (Zhang et al. , 2014; Wan et al. , 2018). MOD11A2 and MYD11A2 products are available at a spatial resolution of about 1 km with an accuracy of 1 K under clear-sky conditions  (Wan, 2013). MODIS lake surface temperature data are pre-processed to account for atmospheric and surface emissivity effects. The cloud mask (MOD35) used for inland water provides a surface temperature measurement when there is a 66 % or greater confidence of clear-sky conditions  (Wan, 2013) ; otherwise no temperature is produced. To reduce the contamination from land pixels, only lake pixels beyond 1 km of the shoreline were extracted  (Ke and Song, 2014).
At Aru Co, lake surface temperature in the southern and northern (closer to the ice avalanches) halves of the lake was extracted to investigate its spatial difference (Fig. 2 ). At Memar Co, lake surface temperature in the northern half of the lake was extracted. Anomalous lake surface temperature was examined and removed if there was a big difference between MOD11A2 and MYD11A2 datasets. To confirm the reliability of MODIS products, nighttime lake surface temperature was compared with in situ observation at the shoreline.
This indicates that Aru Co, as an outflow lake, plays a significant role in regulating the water balance of Memar Co. The impact of the two glacier collapses on lake level changes can be found from the seasonal lake level changes derived from CryoSat-2 satellite data and in situ observations between 2011 and 2019. The lake level increase in the cold season (October to May) did not vary much from year to year, with an average value of 0. 35 m before  (i. e. , 2011-2015) and 0. 36 m after (i. e. , 2016-2019)  the glacier collapses (Fig. 6a ). However, lake level increase in the warm season (May to September, referred to as "summer" in the following) increased drastically after the glacier collapses (Fig. 6b ). Before the glacier collapses, lake level increase in the warm season varied in a range of -0.
Impact of the meltwater on the inter-annual lake level changes in Memar Co Lake level changes at Memar Co between 1972 and 2018 were investigated based on satellite altimetry data and bathymetry survey. Lake level changes between 2003 and 2018 were observed by ICESat and CryoSat-2 satellite altimetry. Earlier lake level changes before 2003 were reconstructed according to the bathymetry survey and past shorelines  (Lei et al. , 2012). Bathymetry survey showed that the lake level of Memar Co was 10. 4 ± 0. 6, 12. 3 ± 0. 3, 12. 5 ± 0. 3, 8. 3 ± 0. 3 and 3. 1 ± 0. 3 m lower in 1972, 1994, 1999, 2004 and 2014  relative to October 2018. According to the lake area and the corresponding water level in the 5 years, the relationship between lake area and water level was developed by using a second-order polynomial regression (R 2 = 0.
99): y = -0. 1077 × x 2 + 2. 8468 × x + 176. 81. (1) Here, y is the lake area (km 2 ) and x is the lake water level (m). Thereby, we reconstructed lake level changes since 1972 by using the corresponding lake area (Fig. 7 ). To validate the results, we compare the reconstructed lake level changes with satellite altimetry data during the overlap period of 2003-2018. Figure  7a  shows that there is a good correspondence between the two datasets, indicating our reconstructed lake level changes are reliable. Based on lake area and water level changes, lake dynamics of Memar Co between 1972 and 2018 can be divided into two distinct periods (Fig. 7 ). Between 1972 and 1999, Memar  Co exhibited gradual shrinkage with a lake level decrease of 2. 1 ± 0. 3 m. Since 2000, Memar Co experienced rapid expansion with a lake level increase of 12.
S5 ). This is because MODIS sensors measured the lake skin temperature at the lake center, while a HOBO logger measured lake water temperature at the depth of 30-70 cm at the shoreline. The Aru-1 ice avalanche significantly affected the lake surface temperature of Aru Co and Memar Co in summer 2016. Both Terra and Aqua datasets showed that nighttime lake surface temperature at Aru Co decreased abruptly by 2-4 • C in the first 2 weeks after the Aru-1 glacier collapse (Fig. 8 ), which was quite different from normal years and may have considerably affected the lake ecosystem. A similar decrease in lake surface temperature also occurred at Memar Co but with less magnitude and a shorter duration than at Aru Co (Fig. 8 ). We attribute the significant decrease in lake surface temperature to the ice floes over the surface of Aru Co.
In 2003, the water level of Aru Co (4936. 8 m a. s. l. ) was about 14 m higher than that of Memar Co (4923. 2 m a. s. l. ), as indicated by ICESat satellite altimetry data. In 2014, CryoSat-2 data show that the elevation difference between the two lakes decreased to ∼ 8 m due to the continual lake expansion of Memar Co. After the glacier collapses, Memar Co expanded at an accelerated rate and the elevation difference became even smaller. In October 2019, the surface elevation of Memar Co reached 4931. 3 m a. s. l. and the elevation difference between the two lakes decreased to only 5. 5 m. Projection in the near future of the increasing rate of 0. 5-0. 8 m yr -1 between 2003 and 2019 indicates that the water level of Memar Co could reach that of Aru Co in 7-11 years. According to the reconstructed relationship between lake area and lake level in Sect.
Acknowledgements. We thank  USGS  for providing Landsat data and Sentinel data,  NASA LAADS  for providing MODIS LST data, and the  China High-Resolution  satellite data sharing platform for providing Gaofen-2 data. We are also grateful to all the agency members who took part in the fieldwork. We also thank the three anonymous reviewers and  Linghong Ke  for their constructive comments which have improved this paper. Financial support. This research has been supported by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (grant no. XDA2006020102 ), the  Second Tibetan Plateau Scientific Expedition and Research Program  (grant no. 2019QZKK0201 ), the  National Science Foundation of China  (grant nos. 41971097  and  21661132003 ), the  Youth Innovation Promotion Association of the Chinese Academy of Sciences  (grant no.
Data availability. Landsat 8 OLI data and Sentinel-2 data can be accessed at http://glovis. usgs. gov (last access: July 2019, USGS, 2019). MODIS LST products are available at HYPERLINK https: //ladsweb. modaps. eosdis. nasa. gov (last access: July 2019, NASA LAADS, 2019). Any other specific data can be provided by the authors on request. https://doi. org/10. 5194/tc-15-199-2021 The Cryosphere, 15, 199-214, 2021.
Since 2017, the goal of the two-million song WASABI database has been to build a knowledge graph linking collected metadata (artists, discography, producers, dates, etc. ) with metadata generated by the analysis of both the songs' lyrics (topics, places, emotions, structure, etc. ) and audio signal (chords, sound, etc. ). It relies on natural language processing and machine learning methods for extraction, and semantic Web frameworks for representation and integration. It describes more than 2 millions commercial songs, 200K albums and 77K artists. It can be exploited by music search engines, music professionals (e. g. journalists, radio presenters, music teachers) or scientists willing to analyze popular music published since 1950. It is available under an open license, in multiple formats and with online and open source services including an interactive navigator, a REST API and a SPARQL endpoint.
Since 2017, the WASABI research project 1 has built a dataset covering more than 2M songs (mainly pop/rock and dub) in different languages, 200K albums and 77K artists. Musicologists, archivists from Radio-France, music schools and music composers also collaborated. While cultural data were collected from a large number of data sources, we also processed the song lyrics and performed audio analyses, enriching the corpus with various computed metadata addressing questions such as: What do the lyrics talk about? Which emotions do they convey? What is their structure? What chords are present in the song? What is the tempo, average volume, etc. ? We partnered with the Queen Mary University of London (QMUL) and the FAST project 2 for extracting chords from the song audio, and linked to IRCAM's TimeSide 3 audio analysis API which for audio processings (beat detection, loudness, etc.
). We deployed REST and SPARQL endpoints for requests and a GUI for exploring the dataset  [6]. The dataset, Machine Learning models and processing pipeline are described and available 4  under an open license. 5  Section 2 presents the context of the WASABI project and related works. In section 3, we explain the way we collected and processed data to build the corpus. Section 4 focuses on the formalization, generation and publication of the RDF knowledge graph. Section 5 presents several tools and visualizations built on top of the dataset and services. Finally, section 6 discusses quality assessment concerns while sections 7 and 8 discuss future applications and potential impact of the dataset and conclude with some perspectives.
State of the art and related work There are large datasets of royalty-free music such as Jamendo (often used  [5, 26] ) or others found in the DBTunes link directory, but we focus on the ones that cover commercial popular music (see Table  1 ) and we will see that few propose metadata on cultural aspects, lyrics and audio altogether. MusicBrainz offers a large set of cultural metadata but nothing about lyrics, for example. The Last. fm dataset contains tags that were used by some researchers for computing moods and emotions  [13, 7]. AcousticBrainz, a public, crowd-sourced dataset, contains metadata about audio and has been used by projects such as MusicWeb  [2]  and MusicLynx  [3]  to compute similarity models based on musical tonality, rhythm and timbre features. The Centre for Digital Music of QMUL collaborated with the BBC on the use of Semantic Web technologies, and proposed music ontologies in several fields including audio effects and organology.
MusicLynx  [3]  provides an application to browse through music artists by exploiting connections between them, either extra-musical or tangential to music. It integrates open linked semantic metadata from various music recommendation and social media data sources as well as content-derived information. This project shares some ideas with the WASABI project but does not address the same scale of data, nor does it perform analysis on audio and lyrics content. The Million Song Dataset project (MSD) processed a large set of commercial songs to extract metadata using audio content analysis  [4] , but did not take advantage of structured data (e. g. from DBpedia) to address uncertainties. Information such as group composition or orchestration can be very relevant to informing Music Information Retrieval (MIR) algorithms, but is only available in certain data sources (BBC, MusicBrainz,.
), and for many little-known artists this information is not available. It is here that the combination of audio and semantics finds its purpose, one reinforcing the other. The WASABI project provides a wider scope than the Million Song Dataset: it started as a challenge to build a datataset that would be twice as big with public domain development of open source tools and a richer cultural and lyric-related set of metadata. The DOREMUS project  [16]  overlaps with WASABI but in a rather different context (classical and traditional music). DOREMUS performs the integration of MIDI resources (instead of MIR analysis), recommendation and automatic playlists generation. The WASABI ontology extends the Music Ontology (MO), yet the Performed Music Ontology 6  (part of LD4L) or DOREMUS ontology (based on FRBR) may be considered if future works need to model more accurately the differences between works, performances or expressions.
The Listening Experience Database (LED) collects people's music listening experiences as they are reported in documents like diaries or letters  [1]. It mostly relates to legacy music that has little overlap with WASABI. The MELD framework  [21]  supports the publication of musicology articles with multi-modal user interfaces that connect different forms of digital resources. Some development could be undertaken to allow musicologists publish articles that would leverage musical data from the WASABI RDF knowledge graph. The MIDI Linked Data project  [17]  publishes a large set of MIDI files in RDF. Linked to DBpedia and relying on the Music Ontology, it could complement WASABI to jointly exploit MIDI files and audio and text analyses. Some MIDI content was used in WASABI during the evaluation of the chord extraction.