dataesr/scientific-paragraphs-categorization huggingface dataset:
SNEWS carried out the initial development which began in Phase 0 (Figure  3 ). During this stage, SNEWS developers attended the SCiMMA 2020 Virtual Workshop software demonstration session. 45 This allowed the SNEWS developers to utilize the SCiMMA hop-client application template, a customizable interface to HOPSKOTCH, as the basis to prototype SNEWS 2. 0 using SNEWS's previous software requirements. They extended the template with additional SNEWS-specific functionality, maintained the codebase in a private GitHub repository, and ran local tests in Python. During development, SCiMMA added the SNEWS developers to the SCiMMA Slack 55 channel to provide user support and debugging as SNEWS developed the prototype, though the development itself was driven entirely by SNEWS. This phase resulted in a successful redeployment of SNEWS's basic functionality using SCiMMA's hop-client.
Once SNEWS developed their prototype to the extent possible with hop-client, SNEWS presented a status update at SCiMMA's July 2020 Public Teleconference. 56 This presentation highlighted SNEWS's current implementation, usage requirements, and desired features as realized during Phase 0 (Figure  3 ). Some requested features were not currently in SCiMMA's software, such as a database for message storage or a customizable message format. The SNEWS team considered three options: develop the missing requirements themselves, wait until SCiMMA developed these requirements in their own timeline, or seek a collaboration with SCiMMA to develop the requirements together. After this status update, it was not clear which path forward SNEWS should take.
The team created an Epic and User Stories specifically for SNEWS development support, and assigned this project to two developers. These SCiMMA developers remained active in core SCiMMA tasks and assessed the SNEWS codebase, but did not commit any time toward SNEWS software development during the remainder of Phase 0. After SNEWS's presentation to SCiMMA and the SCiMMA developers' internal sprint presentation, it became clear that a collaboration would support SNEWS's development goals while providing an informative use-case for SCiMMA's own development. While there were still concerns about allocating effort away from core development, SCiMMA proposed a short period of focused collaboration with SNEWS (Phase 1 in Figure  3 ) given the many synergies between the projects. Both groups then decided to actively work together to further the development of the SNEWS 2.
Concerning communication during a collaboration The assessments during Phase 0 confirmed both that HOPSKOTCH was a valuable technology for supporting SNEWS 2. 0 and that SCiMMA was willing to work with SNEWS to meet their requirements and timeline, suggesting that a collaboration would be mutually beneficial. However, given the concerns of each organization, the structure of the collaboration would need to meet several constraints. Foremost, the workload would need to be shared between organizations. This would ensure that SNEWS would have the understanding required to sustain the codebase after SCiMMA's involvement, allow the SCiMMA developers to understand the existing SNEWS codebase on a short timescale, and avoid software duplication. This collaboration would also change SCiMMA's previous community engagement model, wherein MMA organizations served only as end-users rather than collaborative developers.
Each phase proceeded only after a consensus between organizations was reached, so that the conclusion of each phase allowed each organization to build trust and evaluate whether to continue the collaboration. This approach ensured that the collaboration was addressing the evolving needs and perspectives of each organization. This collaboration was composed of two members of SCiMMA and four members of SNEWS, from the working groups pictured in Figure  2. In this collaboration, the Scrum Master was a part-time SCiMMA software developer who primarily worked on gravitational-wave computing cyberinfrastructure. There were two SNEWS Product Owners, one primary investigator and one graduate student from particle astrophysics domains. The Scrum Master and Product Owners identified the specific User Stories that were to be tasked during each sprint.
The entire team included the Scrum Master and Product Owners, a second software developer from SCiMMA, an undergraduate student developer from SNEWS, and an academic IT specialist from SNEWS. This composed the core Scrum team, which coordinated sprints via sprint planning sessions, sprint retrospective meetings, and daily check-ins. The Stakeholders included six primary investigators in total, three from SCiMMA and three from SNEWS, who served to evaluate the phases and decide if a new phase should be generated. Due to the geographically distributed, remote nature of the collaboration, all Scrum and Stakeholder meetings were conducted via Zoom, 57 check-ins were facilitated via the online real-time messaging system Slack,  55  and User Story management was conducted via a GitHub Project Board (Figure  4 ).
The SNEWS Product Owners were responsible for ensuring that the SNEWS 2. 0 prototype was within specifications, communicating the needs of SNEWS to SCiMMA during sprints, and reporting the collaboration progress during SNEWS's internal meetings. The other developers worked on improving the prototype and deploying it on test systems. This framework ensured bilateral effort for the collaboration and provided regular communication between the organizations. It gave SNEWS an active role in the development, allowing for feature requests and bugs to be relayed through the SCiMMA developers to the ongoing SCiMMA sprint. This also allowed for SNEWS to become familiar with the software, reducing the risk of unsustainable software after the collaboration. The Agile Scrum framework also supported timely development by allowing large goals to be translated into smaller tasks, promoting iterative development.
Addressing timeline constraints Phase 1 concluded with a demonstration of the software to the SNEWS and SCiMMA stakeholders. After reviewing the collaboration progress, it was clear that there was mutual benefit and that the initial concerns appeared to be ameliorated in a manageable way. Both organizations agreed to a second, 4-week period of focused development (Phase 2 of Figure  3 ). However, at the end of Phase 1, there were clear timeline constraints: SNEWS was seeking to begin the transition to production operation of the software and SCiMMA expected to focus more effort on the internal development tasks that had resulted from the collaboration. These timelines were constrained by the funding available for each project independently. These new constraints prompted several changes to the collaboration structure.
Measurements of success The main goal of Phase 0 was to rewrite the current SNEWS publish-subscribe system using the hop-client 48 (Figure  5 ). Though an Agile framework was not used during this phase, Phase 0 served a similar role to the Agile Inception Phase: planning and deciding on software requirements was the primary goal. Phase 0 was marked successful by the demonstration of a working prototype that included sample publishing and subscribing of F I G U R E 5 Illustration of the goals and outcomes for each phase of the SNEWS-SCiMMA collaboration. Each phase proceeded after a progress review and consensus within each organization. messages with the SNEWS decision making framework. 56 This established the system's baseline prior to any collaborative development and gave SNEWS the confidence to rely on external software.
Future directions After the demonstration and evaluation of Phase 2, the collaboration had successfully produced a prototype of SNEWS 2. 0 using SCiMMA's hop-client publish-subscribe system. 12 A schematic of the prototype is shown in Figure  8. The prototype established directions for future development for each group, allowing the organizations to remain in contact even after the phases of focused collaboration had concluded. It has provided direction for SCiMMA's authentication and permission management infrastructure, and has helped SNEWS begin prototyping their detector network using the SCiMMA hop-client. The next steps after the development of the prototype include having SNEWS members from outside the development team test the software using tutorials provided by the SCiMMA developers to ensure that the software is user-friendly.
After this testing, SNEWS plans to integrate all of its neutrino experiments into the SNEWS 2. 0 network. This will happen during a dedicated workshop wherein SNEWS will implement Agile methods, similar to those utilized during Phase 2 to drive the development. Specifically, the workshop will take place over a 4-week sprint and will include daily check-in meetings to discuss progress. Additionally, Epics and User Stories will be defined to keep the integration plans on track. The collaboration promoted the general development efforts for each group. SNEWS utilized SCiMMA's systems and software engineering expertise to quickly develop their prototype, allowing SNEWS to avoid the difficulties of scientific computing and software development. The collaboration also unexpectedly accelerated the pace of SCiMMA's core development since SNEWS' use-case contained features that were of broader MMA interest.
In particular, the sprint and phase retrospective meetings were crucial to the success of this collaboration by allowing the team to frequently evaluate its progress and the resulting software to ensure that the needs of both SNEWS and SCiMMA were being addressed. The progress reports at the ongoing SCiMMA sprint within phases and the collaboration-wide leadership meetings between phases ensured that the separate sprints remained on track. Having persistent communication channels was also essential since the development team was geographically distributed and entirely remote for the duration of the collaboration. Second, implementing an Agile framework accelerated the trust establishment. Trust in the Agile process itself was also essential to the collaboration, since while the SCiMMA developers were already familiar with Agile methods, the Scrum Master did not have prior experience with running a sprint and the SNEWS developers were entirely unfamiliar with Agile.
The sprint meetings and required daily communication ensured that each member had regular correspondence with the others, which allowed the otherwise unacquainted teams to quickly meet and trust the other remotely. This demonstrated the advantages of Agile to the collaboration, enhancing trust in the sprint process itself. Additionally, the trust establishment from the Agile framework built upon the shared scientific backgrounds that the two teams had: nearly all of the developers were already involved in scientific computing and/or MMA. The Agile sprint process capitalized on this mutual scientific language to further enhance trust and communication regarding the scientific software development effort. In particular, the SNEWS Product Owners were able to rapidly convey the technical alerting requirements of the application to the SCiMMA developers since the developers were already familiar with alert software in another area of astrophysics.
Likewise, the SCiMMA developers brought technical software engineering expertise that allowed them to work with SNEWS computer scientists and system administrators. In particular, they worked with the computer science student developer to enhance the packaging, documentation deployment, and unit testing systems for the software, as well as the academic IT specialist to deploy the software on local hardware. Third, the coordination of two Agile processes was novel to both organizations. We believe that adequate trust evolved nonetheless due to two reasons. First, the Agile process continuously generated measurable artifacts in the form of User Stories. The Product Owners and various stakeholders auditing the process could see, in real time, the progress and thus were willing to increase their trust.
In Reference 2, the authors conduct and report on a survey of scientific development teams and organizations to determine how these teams build software. They report features of scientific development described in their survey that our collaboration also embodies, such as the "trust" in community-maintained software when the community is large, such as SCiMMA's use of public Kafka libraries. 61 Our work is largely complementary, however, as we adopted a specific approach without a controlled comparison with other alternative approaches. As such, our work would constitute a single, successful data point within this previous survey. In Reference 19, the authors survey climate scientists to determine their common software development practices. They find that, philosophically and culturally, many development teams resonate with the tenets of Agile development.
AH served as a SNEWS product owner and provided substantial input during development sprints. AB, AH, MJ, RFL, CDT and RW each provided substantial input during progress reviews after each phase. AB, MJ, and CDT proposed the initial collaboration at a National Science Foundation "Harnessing the Data Revolution" grant proposal meeting. The SCiMMA Agile Scrum team (AB, DC, BC, VE, SE, PG, DAH, MJ, WL, CM, SN, DP, RT, CW, and RW) supported core SCiMMA software development prior to and during the collaboration. Additional SCiMMA members (MWGJ, CK) have continued support of the SCiMMA project. The SNEWS Signal Prediction, Alert Formation, and Follow-up working group members (ALB, SYB, WB, MC, AC, MCM, ADO, DD, AGR, SG, AH, RH, SH, JPK, AK, VK, ML, RFL, SL, ML, MWL, JM, DM, RN, EO, BWP, NJ, AR, JR, TS, JCLT, CDT, CFV, CJV, KEW, LW, XJX, YX) supported SNEWS software development prior to and during the collaboration.
ACKNOWLEDGMENTS This work is supported by the  National Science Foundation  "Windows on the Universe: The  Era of Multi-Messenger Astrophysics" program : " WoU-MMA: Collaborative Research: A Next-Generation SuperNova Early Warning System for Multimessenger Astronomy " through grants  1914448 , 1914409, 1914447, 1914418, 1914410, 1914416, 1914426; and via the " HDR-Harnessing the Data Revolution" program , grant  1940209 ; and with the  "Office of Advanced Cyberinfrastructure " through grant  1934752.
These techniques have excellent discriminatory power and reproducible results. Moreover, MLST can be used to build globally shared databases. Whole-genome sequencing is also an interesting approach, but it remains too expensive and time consuming to be implemented in routine clinical microbiology experiments  6. A main disadvantage of all these approaches is the requirement of extra experimental procedures, in addition to routine species identification, each time a new isolate is collected. In the daily activities of a clinical mycology laboratory, isolates are generally collected over time as the outbreak is investigated. Comparing these isolates with already collected isolates belonging to a given clone requires the implementation of extra typing procedures that consume resources and time.
This additional workflow is not suitable to incorporate into the routine activities of clinical laboratories. A tool enabling the detection of clonal populations in a quick and easy manner using a technique that is already systematically performed on each of the isolates would be invaluable during an outbreak. Currently, routine identification of human, animal and plant pathogenic fungi uses MALDI-TOF-based assays. This method produces a protein profile, considered a species-specific fingerprint of the microorganism, using mass spectrometry. This technique permits high-throughput identification of a wide range of microorganisms by comparing their mass spectra with reference spectra included in a database. In recent years, MALDI-TOF has been shown to be more precise and more accurate than traditional identification relying on macroscopic and microscopic morphological criteria  7, 8.
Selection of the spectra for training versus testing. To increase the statistical reliability, we performed a cross-validation of the CNN with iterative experiments (n = 30) in which 44 isolates (80% of the isolates) were randomly selected to train a neural network with corresponding mass spectra obtained during the first session (S1) of culture. For each of the 30 iterations, the mass spectra corresponding to the 11 remaining isolates (20% of the isolates) were ignored to avoid sharing isolates in the training and testing sections. Instead, for those 11 isolates, spectra were selected from the second session (S2) of cultures obtained one month after the first culture session and were included in the testing set. After every iteration of training/testing, the training data were reset, and a new random selection of 44 isolates for training using the S1 spectra and of 11 isolates for testing using the S2 spectra was performed.
Convolutional neural network (CNN): system architecture and training process. Raw data extracted from the fid files of each of the spectra were treated the same way prior to being included in the CNN. In all, approximately 22,000 intensity values (corresponding to 22,000 m/Z) composing a spectrum were submitted to a three-step preprocessing. First, the baseline was subtracted using the asymmetric least squares smoothing method  16. Second, a Fourier transformation was applied to smooth the spectra to avoid small variations in intensity. Third, peaks were picked by computing the derivative of the intensity and then detecting the sign changes in the spectra derivative  17. Treatment of the spectra is schematized in Fig. 2. A CNN is a special type of deep learning model for processing data that has a grid pattern and is designed to automatically and adaptively learn spatial hierarchies of features from low-to high-level patterns.
One of the limits of using CNNs is that they are highly dependent on the quality of the data and thus the available MS machine. Due to the lower accuracy observed with the BACT-MS, we look in detail at the spectra to visualize the differences between the machines (Supplementary Fig. 1 ) and we examined the machine parameters. Peaks in the lower masses lacked in intensity for both clones and non-clone with the BACT-MS compared to the two other instruments. The calibration of the three machines was comparable, but we noticed that the laser in the BACT-MS machine was older (6 years old compared to 2 and 3 years old for the other machines) and that it had already been used for a larger number of shots (14,179,000 for the SAT-MS, 24,042,000 for the MYCO-MS and 97,705,000 for the BACT-MS). Indeed, it is of great interest to test the model on data acquired on nonlocal mass spectrometers, as it will allow the globalization of the method.
Acknowledgements We thank the medical staff of the pulmonology ICU in the Marseille hospital for pointing out the fungal contamination problem of the surgical masks that gave us a concrete case to investigate and enough materials to use for our CNN testing. We thank  Anne-Cécile Normand  for drawing the Fig. 1 workflow for a better understanding of the experiment, and  Noshine Mohammad  for drawing Figs. 2 and3 explaining the preprocessing treatments of the spectra and the composition of the CNN.
We used the CONEFOR extension in ArcGIS 10. 5 [40,41]  to produce a list of all the edges between patches less than 15 m apart. We arbitrarily decided on 15 m because of the large range of possible values for edge effects and plant dispersal capacity reported in the literature  [34, 42, 43]. We then created landscape graphs on the basis of the edge list, using the igraph package in R software and Qgis  [44] [45] [46]. We explore and discuss the different metrics according to the local context and framed within the ecological context. Lastly, we illustrate the applications of relevant metrics (i) at the landscape scale, using landscapes A, B, and C, and (ii) at the local scale, using three representative nodes of landscape B (Figure  1a ). using the igraph package in R software and Qgis  [44] [45] [46].
Graph Metric Analyses Landscape metrics are quantitative indices that describe compositional and configurational aspects of landscapes, the latter being typically described as discrete entities or patches  [7]. The release of the FRAGSTATS program  [103]  in 2012 revolutionized landscape structure analysis and made landscape metrics the most common tool to understand biodiversity, ecological processes, and functions at the landscape scale  [7, 104]. Today, hundreds of different metrics are used to measure landscape patterns  [105]. However, some of them present problems such as redundancy and high correlation, limited consideration of spatial patterns, and difficulties in interpretation leading to inappropriate inferences  [7, 104, 106]. Although some of these problems are also apparent in graph metrics, graph theory presents great potential for understanding processes and functions at the landscape scale and considering the type of landscape phenomenon (plant dispersal, animal movements, fire propagation, etc.
Given the many approaches to quantify spatial patterns (e. g. , landscape, graph, and surface metrics) and that there is no one-size-fits-all solution  [105] , different complementary approaches may be required for the same study, and R is the program with the highest potential to offer them. However, by reducing technological barriers, the same problems that occurred with FRAGSTATS development, i. e. , the use and misuse of metrics due to the problems mentioned by Gustafson, Kupfer, and Li and Wu are possible  [7, 104, 106]. One of these problems is the assumption that the matrix is homogeneous, when, in fact, it may neither be uniformly uninhabitable nor serve as a total barrier to the movement of a certain organism  [7]. Many graph metrics are based on this binary perspective of the landscape.
Jazz is a musical tradition that is just over 100 years old; unlike in other Western musical traditions, improvisation plays a central role in jazz. Modelling the domain of jazz poses some ontological challenges due to specificities in musical content and performance practice, such as band lineup fluidity and importance of short melodic patterns for improvisation. This paper presents the Jazz Ontology -a semantic model that addresses these challenges. Additionally, the model also describes workflows for annotating recordings with melody transcriptions and for pattern search. The Jazz Ontology incorporates existing standards and ontologies such as FRBR and the Music Ontology. The ontology has been assessed by examining how well it supports describing and merging existing datasets and whether it facilitates novel discoveries in a music browsing application.
Introduction Jazz is an important musical tradition with a worldwide community of millions of people enjoying, playing, and studying it. While it is primarily considered as a genre of Western music, it ✩ This research was funded under the Trans-Atlantic Program Digging into Data Challenge with the support of the UK Economic and Social Research Council (ES/R004005/1), the French National Research Agency (ANR-16-DATA-0005), the German Research Foundation (PF 669/9-1), and the US National Endowment for the Humanities (NEH-HJ-253587-17). occupies a special position due to the central role of improvisation, which is not common in other Western music genres  [1]. Originating as a mixture of traditional African-American styles with popular American music at the beginning of the 20th Century, jazz has evolved into a highly varied and musically complex genre  [2] [3] [4].