dataesr/scientific-paragraphs-categorization huggingface dataset:
Likewise, JWST developers have provided significant contributions to the several affiliated packages. For example, development of the photutils coordinated package for source detection and photometry has largely been led by JWST contributions. JWST developers have also made significant contributions to the specutils coordinated package, which is used for analyzing spectroscopic data, and the regions coordinated package, which is used to handle geometric regions. The gwcs-affiliated package for generalized world coordinate systems was created specifically to handle the complex world coordinate systems needed for JWST spectroscopic data. The synphot affiliated package for synthetic photometry was created at STScI and is a dependency of the JWST exposure-time calculators. Furthermore, the ASDF (Advanced Scientific Data Format) package  (Greenfield et al.
Data Central and the Anglo-Australian Telescope Archive Data Central is one of the five Australian Virtual Observatory 134  nodes, providing both Australian and international astronomers access to and services for surveys of the southern sky. While primarily hosting optical data, including the Anglo-Australian Telescope (AAT) Archive,  135  Data Central also assists theoretical and radio astronomers with hosting and managing their data. Python is widely used by Data Central, and Astropy and its wider ecosystem play a vital role in the initial ingestion, reduction, access, and visualization of the hosted data and services. For example, the AAT archive hosts data from as early as 1974, so astropy. coordinates, astropy. time, and astropy. fits are used to ingest data flowing from the AAT to ensure that a consistent time and coordinate system is provided to users for simpler querying.
Learn Astropy Learn Astropy-is an umbrella term that acknowledges the broad educational efforts made by the Astropy Project, which is led by the Learn Astropy Team. The efforts focus on developing online content and workshops covering astronomy-specific coding tasks in Python. As introduced in Astropy Collaboration et al. (2018) , there are four different types of Learn Astropy content: tutorials, consisting of Jupyter Notebook lessons that are published in HTML format online; guides, which are a series of lessons providing a foundational resource for performing certain types of astronomical analyses; examples, which are snippets of code that showcase a short task that can be performed with astropy or an affiliated package; and documentation, which contains more detailed information about the code base and user interface.
This categorization drives content development, infrastructure choices, and the appearance of the Learn Astropy website. The Learn Team meets weekly to work on creating, expanding, and improving these educational resources. The Learn Team recently relaunched the main website and search interface for Learn Astropy in 2021 with a new infrastructure platform, built around full-text search and interactive filtering functionality, with the goal of making content more easily searchable and discoverable as the Learn Astropy content catalog expands. This work has been supported in part by a grant from the Dunlap Institute. We have adopted Algolia, a search-as-a-service cloud platform, to store the full-text and metadata records of Learn Astropy's content. The new Learn Astropy website is a JavaScript (Gatsby/React) application that uses the Algolia service to power its search and filtering user interface.
Beyond developing and serving educational content, the Astropy Project has been conducting workshops at winter meetings of the American Astronomical Society since AAS Meeting 225 in 2015 January. Up to the start of the coronavirus pandemic, these were full-day in-person workshops with as many as 90 participants and a dozen facilitators from the Project. During the pandemic, these workshops were moved to an online format and split into basic and advanced sessions. Additionally, beginning with the AAS 238 online meeting, the workshops have been expanded to summer AAS meetings. The Learn team finds that the workshop audience is best found at AAS meetings as opposed to more general Python meetings, as the content tends to be more applicable for students and researchers in astronomy and astrophysics.
The Astropy Project recently provided another mode of community engagement at AAS Meetings 235 and 237 by organizing a NumFOCUS Sponsored Projects booth in the AAS Exhibit Hall. Funding for the exhibit hall was provided alternately by NumFOCUS and later by the Moore Foundation funding. The booth hosted a series of Q&A special sessions during AAS Meeting 235 and webinars during the virtual AAS 237 meetings, to provide the general astronomy community information and access to experts on a variety of open-source astronomical tools. The focus of Learn Astropy over the coming months and years is to further improve the interface for sharing educational content on the main Learn Astropy website, and to facilitate the development of new content that highlights astropy and Astropy-affiliated package functionality.
Current and Future Challenges Over the history of Astropy, the needs and challenges of the Astropy Project have evolved, but many common themes have persisted and are shared by other open-source software communities. However, Astropy also faces a number of unique challenges stemming from the fact that the Project is in a special position in the astronomy software stack: the Astropy code and ecosystem are more specialized than core scientific tools like numpy and scipy, but more general than software packages developed to enable research in specific subdomains such as exoplanet (Foreman-Mackey et al. 2021). Below we discuss a few challenges faced by the Project in more detail, but we stress that this list is not complete or exhaustive. 1. Supporting an active group of maintainers: Throughout its history, the development and maintenance of code and educational materials in the Astropy Project-the astropy core package, coordinated packages, Learn Astropy repositories, and Project infrastructure packages-has been done by a mix of volunteer effort and institutional support.
At present, the named roles and maintainer positions in the Astropy Project are also split roughly evenly between these types of contributors. As is the case with many other opensource software communities, the culture of the Project and overall coordination relies heavily on volunteer effort. We therefore have significant amounts of critical infrastructure or core functionality that is either maintained with no redundancy or unmaintained as contributors naturally move on from and stop contributing to the Project. A prime example within the core package is the astropy. fits subpackage, which is largely in maintenance mode and maintained by just one contributor (despite the significant code base and user community behind this subpackage). While we now have funding to support the longer-term sustainability of the Project and thus to address these issues, we are finding it difficult to use this funding in ways that address our needs.
While Astropy is used globally-for example, Figure  5  shows the number of users per country who accessed the astropy core package documentation in 2021-our user community is underrepresented in non-English-speaking or -teaching countries, and our maintainers and coordinators are fairly narrowly concentrated in the United States and Europe (see the circle markers on Figure  5 ). The gap in users in many countries is understandable given that much of our content and documentation is written in English and therefore only available in other languages through imperfect, automated translation services (like Google translate). The fact that our maintainers and coordinators are generally in the US (even then, mostly on the East Coast) and Europe is a reflection of where institutions have explicitly supported Astropy, and where volunteers have had the ability to contribute to the Project.
3. Long-term and sustained funding for maintaining infrastructure: The software and packaging infrastructure within the Astropy Project includes (but is not limited to) Continuous Integration (CI), test plugins, documentation builds, and package delivery. Keeping the infrastructure working is essential in software development and operations. This kind of work has low visibility and is not attractive to scientists or volunteer contributors; therefore, it is most effective to delegate such tasks to paid roles. Astropy secured a 3-year grant for this but the situation remains uncertain once the grant runs out, as infrastructure evolves and requires constant maintenance and upgrades. Some of the engineers currently in these roles are also employees of research institutions that may leave the Project without much notice, adding to the uncertainty factor.
Conclusions The Astropy Project has continued to serve as an active, community-oriented software project with a wide reach and growing impact, both within astronomy and astrophysics and in broader scientific software communities. The Project is in the midst of setting up infrastructure to ensure its long-term sustainability and to continue the healthy growth of the software ecosystem, developer, and user communities that interface with the Project. For example, over the last years, we have formalized and implemented a new governance model for the Astropy Project (Section 3. 1), have continued to engage a growing pool of active and top contributors (Section 3. 2), and have begun to receive significant formal funding for the Project itself (Section 3. 4). The Astropy Project is also expanding its educational and community-focused initiatives by soliciting and funding initiatives in inclusion, diversity, and equity (Section 3.
3) as well as through the Learn Astropy initiative, which aims to make tutorials and educational content that highlight software tools for astronomy more discoverable and accessible (Section 4. 5). We expect to continue in all of these threads over the coming years as we aim to make the Astropy Project more sustainable and a larger, more welcoming community for open-source software users and enthusiasts in astronomy. Harvard-Smithsonian Center for Astrophysics, and the South African Astronomical Observatory. Furthermore, the Astropy packages would not exist in their current form without a number of web services for code hosting, continuous integration, and documentation; in particular, Astropy heavily relies on GitHub, Azure Pipelines, CircleCI, and Read the Docs. This research has made use of NASA's Astrophysics Data System.
We would like to thank the members of the community who have contributed to Astropy, opened issues and provided feedback, and supported the Project in a number of different ways. We acknowledge the  Gordon and Betty Moore Foundation  for their continued financial support. This work is partially supported by  NASA  under grant No. 80NSSC22K0347  issued through the  NASA ROSES program. This work is partially supported by the international  Gemini Observatory , a program of  NSF 's NOIRLab, which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation , on behalf of the  Gemini partnership of Argentina, Brazil, Canada, Chile , the  Republic of Korea , and the  United States of America. We also thank  NumFOCUS  and the  Python Software Foundation  for financial support.
J. A. A. -M. acknowledges funding support from  Macquarie University  through the  International Macquarie University Research Excellence Scholarship ("iMQRES "). A. B. was supported by the  Lendület Program of the Hungarian Academy of Sciences , project No. LP2018-7 , and the  KKP-137523 "SeismoLab" Élvonal  grant of the  Hungarian Research, Development and Innovation Office (NKFIH). M. B. gratefully acknowledges support from the  ANID   BASAL  project  FB210003  and the  FONDECYT regular grant   1211000. F. D. E. acknowledges funding through the  H2020 ERC Consolidator  grant  683184 , the  ERC Advanced  grant  695671  " QUENCH " and support from the  Science and Technology Facilities Council (STFC). S. G. made contributions to Astropy as part of the  Google Summer of Code 2021. S. G. acknowledges some intellectual support from the  CCAD-UNC-NOVA.
N. S. acknowledges support from the  National Science Foundation  through the  Graduate Research Fellowship Program  under grant  1842402. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the  National Science Foundation. Supported by the international  Gemini Observatory , a program of  NSF 's NOIRLab, which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation , on behalf of the Gemini Observatory partnership: the  National Science Foundation  ( United States ),  National Research Council (Canada) ,  Agencia Nacional de Investigación y Desarrollo (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación (Argentina) ,  Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil) , and  Korea Astronomy and Space Science Institute (Republic of Korea).
(2022b) reproject reproject Thomas Robitaille Saba saba Michele Costa sbpy sbpy Michael Mommert, Michael S. P. Kelley, Miguel de Val-Borro, Mommert (2019) Jian-Yang Li sncosmo sncosmo Kyle Barbary Barbary (2014) spectral-cube spectral-cube Adam Ginsburg Ginsburg et al. (2019) SpectraPy https://pypi. python. Marco Fumana Fumana (2021) org/pypi/ specutils specutils Nicholas Earl, Adam Ginsburg, Erik Tollerud spherical_geometry spherical-geometry Bernie Simon statmorph statmorph Vicente Rodriguez-Gomez Rodriguez-Gomez et al. (2019) stingray stingray Daniela Huppenkothen, Matteo Bachetti, Abigail Stevens, Huppenkothen et al. (2019), Huppen- Simone Migliari, Paul Balm kothen et al. (2019) synphot synphot Pey Lian Lim STScI Development Team (2018).
Epigenetic profiles We investigated the epigenetic status of the identified TE copies, considering small RNAs, and chromatin marks. The small RNAs which sizes range from 15 to 35 nucleotids were taken from Lister et al. [16] , for which mapped data were available. There were 4. 17%, 20. 14%, 16. 95%, and 60. 44% of matching TE copies from the Duster-specific, Brassicaceae-specific, TAIR10-specific, and TAIR10 TE datasets, respectively, in the intersection between this dataset and our annotations, indicating a low targeting by small RNA of the method-specific annotation compared to TAIR10 annotated TEs. We analyzed nine epigenetic marks from Luo et al. [17] , also available as mapped data. The hierarchical clustering algorithm identified distinctly different profiles for genes and for TAIR10 TEs (figure  3B ).
TE conservation in flowering plants We investigated the conservation of TE copies by searching for overlaps with known conserved non-coding sequences (CNSs) identified in previous studies. We compared the TE copies with CNSs identified in crucifers  [21]  and rosids  [20]. For both datasets, a substantial proportion of the TE copies overlapped with these CNSs (5. 32 to 19. 8%, see table  2 ). Some TE sequences overlapped with CNSs conserved in 12 rosid species: Arabidopsis thaliana, Carica papaya, Glycine max, Malus domestica, Populustrichocarpa, Fragariavesca, Medicago truncatula, Lotus japonicus, Theobroma cacao, Ricinus communis, Manihot esculenta, and Vitis vinifera. Fossil rosids dating back to the Cretaceous period, estimated by a molecular clock between 125 and 99. 6 million years ago  [34] [35] [36] , have been found.
Our findings therefore reveal a remarkable conservation of 1521 and 1213 TE insertions identified by the Duster and Brassicaceae methods, respectively, over more than 100 million years, twice as many as can be detected with the traditional annotation approach available for the TAIR10 TE annotation. We also show here that the Duster approach can detect more TEs overlapping with CNSs than the Brassicaceae method. The 65 rosid CNSs from the 12 rosid species associated with Duster-specific copies in the 500 bp upstream of genes included 58 Duster-specific sequences. A MEME-ChIP  [22]  analysis of these sequences identified a   IDD5 ) with all q-values < 5 x 10 -2 , described in the JASPAR database  [36]  as C2H2 zinc finger factors of the Dof-type. Interestingly, some of these motifs were found to be related to: (i) AT3G55370 (OBP3), which is known to encode a nuclear DOF domain-containing TF expressed primarly in roots that is responsive to salicylic acid in leaves and petals, (ii) AT1G69570 (CDF5), which is invoved in flower development and photo-periodism, (iii) AT1G29160 (COG1), which acts as a negative regulator in phytochrome-mediated light responses, (iv) AT3G50410 (OBP1), which acts as a positive regulation of transcription and play an important roles in plant growth and development and (v) AT5G02460.
1, which is probably involved in early processes in vascular development. We then looked in detail at the conservation of Duster-specific, Brassicaceae-specific, and TAIR10-specific copies in the Brassicaceae. We considered only regions close to orthologous genes found with OrthoMCL (see Materials and Methods). We focused on A. thaliana, A. lyrata, C. rubella, and S. parvulum, as these species have divergence times of 5 to 40 My. Overall, we found 6265 groups of four orthologous genes (one gene for each species) containing a TE copy annotated (available in one of the TAIR10 TEs, Brassicaceae, or Duster datasets) within the gene extended by 500 bp upstream (29% of A. thaliana genes). Regions encompassing both the orthologous gene and its upstream sequence were pairwise aligned with the cognate A.
thaliana region. Within this 6265 regions, we found 2457, 353, and 11 TE copies from respectively Duster-specific, Brassicaceae-specific, and TAIR10-specific datasets. We considered a TE copy to be present if more than 50% of the A. thaliana annotated TE copy nucleotides were identical in the pairwise alignment. The TEs were oldest in the Duster-specific set, followed by the Brassicaceae-specific set, as shown by the height of the "111" bars of the histogram, which corresponds to the presence of a TE at orthologous positions in all four species (Figure  4 ). Interestingly, the "000" bars was also quite high. This bar corresponds to TEs found only in A. thaliana, but which belonged to method-specific sets and therefore escaped the reference A. thaliana TE annotation or TE detection by the simple REPET de novo procedure limited to A.
The result depends on sequence length and on a model that is mathematically sophisticated, but too simple biologically, in that it considers successive nucleotides to be independent and equally probable. We now know that neither of these assumptions hold true for real sequences. Consequently, the model is of debatable value and may reject some alignments differently according to sequence lengths. In Duster, we retain all regions that match two k-mers, and the empirically chosen parameters yielded very few false positives (0. 001). We can see here that BLAST is not the most appropriate algorithm for finding small degenerate TEs. It was developed for a different purpose: identification of the best match within databases to a sequence used as a query. Its use to identify TEs constitutes a major deviation from its initial purpose, for which it performs well.
Acknowledgements We thank  Klaas Vandepoele  for reading a first version of this manuscript and drawing our attention to interesting TFBS and CNS data that enabled us to improve the manuscript. We also thank  Michaël Alaux  and  Johann Confais  for their comments on the manuscript. This work was performed with the facilities of the  Plant Bioinformatics Facilities  (https://doi. org/10. 15454/1. 5572414581735654E12 ). Version 6 of this preprint has been peer-reviewed and recommended by  Peer Community In Genomics  (https://doi. org/10. 24072/pci. genomics. 100004).
Mesh The FRFs were measured on a mesh of 70 to 170 points, depending on the MWC, distributed over the whole MWC structure. Each mesh point was highlighted by coloured stickers fixed to the MWC part. To determine the coordinates of each mesh point, a 3D scan was performed (iSense iPad 4G, 3D Systems) to obtain a coloured 3D model of the MWC. Based on this model, a MATLAB routine was developed to extract 3D coordinates of the impact points from the 3D mesh through a selecting and colour identification procedure.
Experimental Modal Analysis Force data were segmented using a uniform 10 ms rectangular window centred on the hammer impact. An exponential decay window was applied to the acceleration signals to reduce leakage  [11]. The quality of the data was then verified using the coherence function  [11]. To obtain the eigenfrequencies, damping ratios, and mode shapes, the FRFs, H ij , were calculated as: H ij ( f ) = A ij ( f ) F i ( f ) ( 1 ) where f is the frequency vector, A ij is the acceleration measured by the accelerometer j for an impact in point i, and F i is the impact force at point i. The FRFs were analysed between 4 and 80 Hz, according to the seated human body's sensitivity response to vibration [6]. To calculate the FRFs, each mesh point was successively excited with an impact hammer (086C02, PCB Piezotronics, 11.
2 mV•N -1 , ±444 N•pk, resonant frequency ≥15 kHz, sampling rate 25,600 Hz). The excitation was repeated as many times as required to obtain a force signal with an acceptable bandwidth (i. e. , a band limit higher than 200 Hz), a low noise level, and no secondary impact. The resulting normal acceleration was measured by one synchronized mono-axial accelerometer (352A24 Accelerometer, ICP ® , PCB Piezotronics, Buffalo, NY, USA, 100 mV•g -1 , ±50 g•pk, [1-8000 Hz], sampling rate 25,600 Hz) fixed on the part studied and aligned in the direction of interest. For each MWC part, the frequencies were firstly visually identified (i. e. , the FRF phase change associated with an FRF peak). Based on the identified frequencies, the modal properties were identified using the Least Squared Complex Frequency domain (LSCF) method  [12, 23]  implemented in the Structural Dynamic Toolbox  [23]  running on MATLAB R2019b.
2. MeshThe FRFs were measured on a mesh of 70 to 170 points, depending on the MWC, distributed over the whole MWC structure. Each mesh point was highlighted by coloured stickers fixed to the MWC part. To determine the coordinates of each mesh point, a 3D scan was performed (iSense iPad 4G, 3D Systems) to obtain a coloured 3D model of the MWC. Based on this model, a MATLAB routine was developed to extract 3D coordinatesMWC08 X X X X X MWC09 X X X X X X MWC10 X X X X X X MWC11 X X X X X X.
Any companions less than that mass would be undetectable in the spectroscopic data and any companions that were more massive would have been detected. (Bottom) Minimum detectable mass as a function of period for selected inclinations. claim to have measured a value of log(R HK ¢ ) = -4. 016. The methodology behind this claim was to measure the surface flux R HK by coadding all available spectra for the target into a template spectra that was then normalized to a PHOENIX model atmosphere in order to convert to absolute flux units. The photospheric flux contribution R F T phot phot eff 4 s = was then subtracted from the integrated flux of the Ca II H and K line cores from the PHOENIX model atmosphere. The excess that was seen after this subtraction interpreted as being from emission at the H and K lines.