dataesr/scientific-paragraphs-categorization huggingface dataset:
All tracks from the 10 CDTMs have been used to build the composite tracks in a three-step procedure that is summarized in the flowchart shown in Fig. 1. -First step. The algorithm starts from the first track and searches for all other "similar" tracks within the dataset containing all the tracks from all the CDTMs. Two tracks are identified as "similar" if their track points overlap in space and time. Since the overlap of two tracks is unlikely to be perfect, space and time threshold criteria are applied. In terms of space, two track points belong to similar tracks if they occur at the same time and are no more than 300 km apart. This distance threshold was chosen as a length order of the minimum radius of Mediterranean cyclones  (Campins et al. , 2011; Reale et al. , 2022). Identified cyclone centers further apart than 300 km could belong to either distinct cyclones or distinct centers, nested within relatively large cyclonic circulations.
For example, Fig. 2b  includes 15 cyclone tracks in total, where several methods captured one track in the western Mediterranean and a second one over the eastern Mediterranean, which overlap with the same rather long track of M07. -Second step. In this step, composite track points are created at the average locations of all track points identified as similar in step 1 (i. e. , track points that share the same time and are not 300 km apart). The number of methods used to create composite track points defines the "confidence level", which ranges from 2 to 10. For instance, a confidence level of 5 suggests that a track point was captured by at least five CDTMs. As such, final composite track datasets of any confidence level are always subsets of datasets with a lower confidence level. The confidence level of the composite track point is depicted by the size of black and red dots in Fig.
Benchmarking the performance of cyclone-tracking methods A major challenge in the field of cyclone tracking is the absence of reference datasets for benchmarking the performance of CDTMs. While best-track datasets are usually issued from forecast services of tropical cyclones, there are no similar datasets for extratropical cyclones and specifically for Mediterranean cyclones. The lack of such best-track datasets can be arguably traced back to the highly variable spatial structure of extratropical cyclones  (Neu et al. , 2013). Indeed, extratropical cyclones may lack the clearly distinguishable centers which are typically found in their tropical counterparts. For instance, in cases of secondary cyclogenesis or cyclone families, a CDTM might detect two cyclones as one storm. Alternatively, a CDTM might only partially identify fast-moving storms and explosive cyclones due to the application of strict constraints in its tracking and detection procedures.
e. , spatial "jumps"), (iii) to only retain tracks that lasted at least 24 h and (iv) to make sure that all tracks had consecutive hourly track points. Subjective cyclone tracking is, by definition, subject to human error and may produce different tracks for the same cyclone systems. Moreover, the final datasets are not reproducible. Nevertheless, it is the subjective criteria that transform these tracked cyclones into "useful" datasets; i. e. , the included tracks would be potentially selected by a researcher for reasons of scientific research and for operational forecasting purposes when high-impact weather is imminent. It is noteworthy that these tracks have been produced using MSLP fields alone. Therefore, cyclones that would be identified in a clearer way by relative vorticity or other atmospheric variables may be absent from these datasets.
In fact, the subjective production of a reference dataset for the ends of CDTM assessment would demand thorough and rigorous investigation of weather maps and the use of more atmospheric variables (wind, geopotential height, etc. ). In our more simplistic approach the level of agreement between the subjective tracks is strongly dependent on the different human perceptions of cyclone centers in each expert meteorologist, given the same atmospheric fields. Taking into account the above, the five datasets should be regarded as a reference for benchmarking the ability of individual CDTMs and composite tracks to capture a certain number of well-known cyclone cases. This number is statistically small compared to the number of cyclones in a 42-year climatology, and consequently benchmarking cannot be considered a token of the general quality of CDTMs.
The procedure of subjective cyclone tracking produced five datasets composed of 68, 59, 73, 82 and 97 cyclone tracks. It is noteworthy that many cases in the list of 117 cyclones were not clearly distinguished in MSLP fields of ERA5 or were not tracked for at least 24 h. To quantify the degree of similarity between subjectively tracked cyclones, we apply step 1 of our methodological approach to every pair of datasets: two tracks are defined as similar if they share common track points for a certain threshold time period. Similarity score is then defined as the number of similar tracks, divided by the smaller number of tracks included in either of the two datasets. A similarity score of 100 % implies that the ensemble of tracks included in the smaller dataset may be considered a subset of the other dataset.
Similarity of outputs from individual CDTMs As a measure of performance of the 10 CDTMs, Fig. 8  shows the similarity scores between the produced tracks and the ones in the five datasets of subjectively tracked cyclones. Using an overlapping threshold of 24 h, all methods have consistently tracked more than 65 % of the subjectively tracked cyclones. The same percentage exceeds 75 % when considering a rather small overlapping threshold of 6 h. As expected, it is more likely for the CDTMs to capture smaller segments of subjectively tracked cyclones. The spread of scores in Fig. 8  might significantly vary, even by 30 % (range of similarity scores is depicted by the spread of whiskers in Fig. 8 ). Such a high range plausibly reflects the non-negligible disagreement between the subjectively tracked cyclones (Fig.
4 )  as well as the effect of comparing such a small number of tracks to a 42-year climatology. Therefore, the similarity scores in Fig. 8  are not to be taken as a measure of quality for the 10 CDTMs but as the means to quantify their performance for selected case studies. The results in Fig. 8  are thus a reference of an individual CDTM's performance to better understand the quality of composite tracks as reference datasets in the next section. It is noteworthy that several CDTMs were developed and substantially tested for studies in the Mediterranean region (Appendix A). This could result in a more favorable calibration for Mediterranean cyclones. https://doi. Especially concerning the performance of M07, this is the only CDTM that uses only relative vorticity as an input field, while the manual tracks were identified using MSLP.
Composite tracks compared to other tracks from individual CDTMs We applied our compositing approach described in Sect. 2. 1 to the ensemble of tracks of the 10 different CDTMs using 4 overlapping criteria and 9 confidence levels (from 2 to 10). This resulted in the production of 36 datasets of composite tracks built by combining at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 CDTMs that overlap at least 6, 12, 18 or 24 h. Figure  10  shows the similarity scores between these 36 datasets and the subjectively tracked cyclones. Given our motivation to produce a reference track dataset composed of well-defined and long-lasting intense Mediterranean cyclones, in Fig. 10  we only consider an overlapping threshold of 24 h; i. e. , scores in Fig. 10  show the percentage of subjectively tracked cyclones that overlap with composite tracks in the 36 datasets by at least 24 h.
Clearly, the similarity scores and the number of composite cyclone tracks (shown at the bottom of Fig. 10 ) tend to decrease as a function of the confidence level. This suggests that there is a low probability for a high number of CDTMs to successfully track the same systems. In fact, datasets of higher confidence levels are subsets of datasets with lower confidence levels. For confidence levels up to 7, similarity scores range high, from ∼ 80 % to ∼ 90 %, compared to an average of 75 % in Fig. 8d. It is also noteworthy that similarity scores in Fig. 10    time threshold criteria. It is thus rather plausible to suggest that the overlapping criterion may have a limited effect on the results of Fig. 10. As an exception, similarity scores for an overlapping time threshold of 24 h (brown line in Fig.
On the other hand, confidence level 2 demands only two CDTMs to agree on the detection of a specific track. This naturally yields the lowest percentages for all CDTMs in Fig. 11  with M04, M08 and M10 contributing to the composite tracks of confidence level 2 only by 27 %. In fact, it is more likely for a low number of sensitive CDTMs to detect shallow or weak cyclonic systems, whereas another CDTM would fail if it applied more strict criteria. M01 and M03 contribute to more than half of composite tracks even in datasets with low confidence levels. Beyond confidence level 5, all CDTMs contribute to the majority (>50 %) of composite tracks with an average contribution of 85 % to composite tracks of confidence level 7. Finally, results in Fig. 11  seem to be independent of the season although contributions tend to be slightly smaller (higher) in summer (winter) for confidence levels less than 4 (not shown).
, 2010; Flaounas et al. , 2015 Flaounas et al. , , 2022)). Thus, considering the direct relationship of pronounced seasonal cycles of tracks with cyclone intensity, it is plausible to suggest that the bogus tracks which are filtered out due to increasing confidence levels correspond to the weaker systems in the datasets used to build composite tracks. -Cyclone track density. Figure  13  shows the composite track densities for confidence levels of 2, 4, 7 and 10. All panels in Fig. 13  depict very similar patterns, where most cyclones occur in the western and central Mediterranean Sea surrounding the Italian peninsula. Areas of high track densities are also observed in the lee side of the Alps, over the Aegean Sea, close to Cyprus, in the Black Sea and in northwestern Africa. These favorite locations of Mediterranean cyclogenesis are consistent with the ones found in previous studies, regardless of the atmospheric model or CDTM used to produce the tracks  (Lionello et al.
This comes in agreement with a previous analysis of  Lionello et al. (2016) , who showed that filtering out weak and slow cyclones improves the agreement among CDTMs. When comparing confidence levels of 5 and 10 regarding their seasonal cycles (Fig. 12b ), intensity distributions (Fig. 14c ) and track densities, it can be seen that the higher the confidence level, the more intense cyclones tend to concentrate in winter months over the Mediterranean Sea rather than over land areas or close to the mountains (Fig. 13b  vs. Fig. 13d ) where hot spots of track densities are located for most CDTMs (Fig. 6 ). It is thus plausible for datasets of high confidence levels to tend to capture well-defined, longlasting cyclones that travel over maritime areas. This is consistent with  Pepler et al. (2020) , who combined two CDTM methods and showed that pressure lows which were identified by both methods produced higher average rainfall totals than pressure lows which were identified by only a single CDTM (their Fig.
Discussion and conclusion Many CDTMs have been previously used, yielding a substantial number of climatological findings for extratropical cyclones. However, so far research has not been able to produce reference datasets that would render cyclone climatological results from different CDTMs directly comparable to each other and that could be used as a reference to analyze new or updated versions of existing CDTMs. In fact, it is rather difficult to produce such best-track datasets for extratropical cyclones. These systems have a complex morphological nature, varying in size and shape, while several centers could be identified within the vicinity of their single mesoscale to large-scale cyclonic circulation. This complexity is in contrast to tropical cyclones where cyclonic systems are clearly distinguishable from their environment and tend to present a single cloudless center.
To address this gap in the state of the art in the field of cyclone tracking, we propose a method that builds datasets of ranked confidence level by combining outputs from 10 different CDTMs. We especially focus on Mediterranean cyclones, which are rather challenging weather systems to track due to their frequent proximity to complex geographical features such as abrupt land-sea transitions and the long mountainous chains that surround the Mediterranean Sea. These geographical features perturb the atmospheric fields which are used as input datasets to CDTMs. Therefore, CDTMs tend to produce a significant number of bogus tracks that jeopardize the robustness of climatological results. Such bogus tracks might emerge from long-lasting field perturbations close to mountains which are "erroneously" perceived as well-organized cyclone systems or MSLP minima that abruptly develop downstream of a ridge.
In Sect. 2, we described our methodological approach in which composite tracks were produced by combining the outputs of 10 different CDTMs. The number of combined tracks determines the confidence level: the more tracking methods agree on the occurrence of a cyclone track, the higher the confidence level of the composite track. To benchmark CDTMs and composite tracks' performance, five datasets of subjectively tracked cyclones have been produced, each by a different meteorologist. The subjectively tracked cyclones were derived from a list of 117 well-documented cyclone cases (mostly showing tropicallike characteristics). Considering an overlapping criterion of 24 h, the five datasets hardly exceeded a similarity score of 80 %. This suggests that there can be different human perceptions of the locations and displacements of cyclone centers for a non-negligible number of cases.
Proposing composite tracks of high confidence levels as reference datasets has the advantage of tracks concentrating the agreement of many CDTMs. In addition, datasets of high confidence level include more intense systems (Fig. 14 ), and this reduces the potential of including bogus tracks (i. e. , shallow, non-well-organized systems). The shortcoming when using datasets of high confidence levels is the likelihood of omitting cyclone tracks that did actually occur but were not "successfully and consistently" tracked by a number of CDTMs equal to or higher than the demanded confidence level Therefore, to propose composite tracks as a reference dataset, one needs to consider a trade-off between "robustness" and "completeness" of the final dataset. In the absence of ground truth on the "correct" number of Mediterranean cyclones and given the fact that similarity scores in Fig.
In addition, average track locations would risk being far from the locations of minima of MSLP, geopotential or maxima of relative vorticity. As a result, our methodological approach is transferable to global or any regional application where a distance threshold of 300 to 500 km would be sufficient to yield meaningful results regardless of the physical characteristics of the tracked systems. Nevertheless, in any case, the quality and robustness of composite tracks depend on the efficient calibration and number of included CDTMs, respectively. For a "more general approach" to Mediterranean cyclone climatology, we would recommend the use of datasets with confidence levels of 5 to 7. These confidence levels include a sufficient number of cyclone tracks for climatological studies (50-90 tracks per year) and still retain high similarity scores with subjectively tracked cyclones (Fig.
10 ). A confidence level of 8-10 would be more appropriate for studies on cyclone dynamics where composite approaches would analyze the most intense systems even if the number of cyclones per year were small compared to the datasets of other confidence levels. High similarity scores with subjectively tracked cyclones were also achieved by several individual CDTMs but with a much higher number of cyclone tracks per year, which were shown to correspond to systems of weaker intensities and shorter lifetimes, with the tracks often presenting indistinct seasonal cycles. Our composite tracks retain high similarity scores but are shown to correspond to deeper cyclones with consistent seasonal cyclones and yield more meaningful -less "noisy" -track densities than individual CDTMs (Fig. 6 ). For these reasons, our methodological approach gives us confidence that it produces adequate reference datasets that include the most possible well-organized systems of cyclonic circulation and the least possible bogus tracks.
All cyclone centers are identified as grid points with smaller MSLP values than in the eight surrounding grid points. After cyclone centers are identified, the algorithm starts from the deepest cyclone center in the dataset and produces all of its possible tracks by connecting cyclone centers in consecutive time steps -backwards and forwards in time -provided that they are not 250 km apart. Among all possible tracks, the algorithm chooses the ones that present the least average MSLP difference between its track points. The cyclone centers used to create the track are then discarded, and the algorithm continues with building the track of the next deepest cyclone center. M03  (Ziv et al. , 2015)  is a routine that follows  Hewson and Titley (2010) , with some modifications that account for irregular cyclone trajectories.
The vast majority of these tracks are over maritime areas, in particular in the central and western Mediterranean. Nevertheless, tracks are not repeated identically in all five datasets. Indeed, 37 out of 117 cyclones were included in all 5 of them, 75 cyclones were included in at least 3 of them, while 13 cyclone cases from the 117 in the list were not included in any dataset. Table  B1  summarizes the subjective detection rate of the 117 cyclones. The cases not included in any dataset are mostly related to shallow low-pressure systems. The average duration of subjectively tracked cyclones is 37, 40, 54, 37 and 48 h for datasets D01, D02, D03, D04 and D05, respectively. Such lifetimes are indeed comparable to the ones produced by different CDTMs in Fig. 7a. Finally, cyclone intensity distributions are shown in Fig.
B2. They are also comparable to the ones observed in Fig. 7b  although mature stages reach deeper MSLP values than those observed in the distributions based on objectively tracked systems. Data availability. All composite cyclone tracks for different confidence levels are provided as a Supplement in the form of ASCII files. For each confidence level, we provide a separate file that includes a matrix of eight columns and a number of rows that varies among the datasets. Each row corresponds to a single track point, while the eight columns provide the following information: -Column 1 provides a cumulatively increasing index that functions as an identifier of unique cyclone tracks. For instance, all information about the track of cyclone no. 456 is found in all rows starting with the number 456.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd-4-639-2023-supplement. Author contributions. EF conceptualized the composite tracks approach, developed the methodology, performed the analysis and wrote the initial draft. LA, LB, BD, AK, JK, MAP, MDKP, MR, MR, DS and MS provided ERA5 cyclone tracks. EF, LA, SD, PP and ES each produced a dataset of subjectively tracked cyclones using a MATLAB program written by EF. The program was applied to 117 past cyclone cases, all derived from a list that was made available and updated by MMM. This study is the outcome of collaborative work between all co-authors in the framework of MedCyclones COST Action (CA19109). Meetings were held on a regular basis from April 2021 until the submission of this paper.
Acknowledgements. This study is based upon work from  COST Action MedCyclones  ( CA19109 ), supported by  COST  (European  Cooperation in Science and Technology , https://www. cost. eu, last access: 10 July 2023). The authors thank  ECMWF  and Copernicus for making the ERA5 dataset available. The authors are also grateful to the two anonymous reviewers. Their fruitful comments and constructive suggestions have greatly improved the paper. Financial support. Emmanouil Flaounas  has been supported by the  Stavros Niarchos Foundation (SNF)  and the  Hellenic Foundation for Research and Innovation  (H. F. R. I. ) under the  5th Call of "Science and Society" Action   -"Always Strive for Excellence -Theodore Papazoglou " (project no. 7269 ). Benjamin Doiteau  has been supported by  Région Occitanie and Météo-France.
Claudia Pasquero 's part of this work is an outcome of the project  MIUR -Dipartimenti di Eccellenza   2023-2027. María Ángeles Picornell  has been supported by  Ministerio de Ciencia e Innovación -Agencia Estatal de Investigación (Spain)  through the project  TRAMPAS  ( PID2020-113036RB-I00/AEI/10. 13039/501100011033 ). Marco Reale  has been supported in this work by  OGS  and  CINECA  under  HPC-TRES  award number  2015-07  and by the project  FAIRSEA (Fisheries in the Adriatic Region -a Shared Ecosystem Approach)  funded by the  2014-2020 Interreg V-A Italy-Croatia CBC Programme  (standard project  ID 10046951 ). Malcolm J. Roberts  was supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra  ( GA01101 ). Review statement. This paper was edited by  Irina Rudeva  and reviewed by two anonymous referees.
In a standard bioacoustic experiment setting, after data collection and before data analysis lies a time-consuming process that consists in extracting and labeling sounds of interest (chunks) from usually long recording files, collected through spreading PAM (Passive Acoustic Monitoring) for instance, and storing them in a structured and exploitable way based on both the meta-data of the initial recordings and the label data. SoundChunk is a user-oriented package in R that aims at providing tools for any R user so that they can go efficiently and comfortably through this process. Usually, the tasks include detecting, labeling, and extracting sound from audio files. With the recent advance of Machine Learning, especially Convolutional Neural Networks, some of these tasks are integrated in the ML framework; however, the learning phase relies on labeled chunks that need to be created.
Three types of data are involved in the process: (i) WAVE files, whether they come from the initial recordings or are generated by chunk extraction, (ii) meta-data describing the conditions in which the initial WAVE files were recorded, (iii) label track files, whether they are automatically generated or manually input, that contain timed information about audio files. SoundChunk provides utilities to sanely manipulate each of these three categories and combine them into easy-to-use chunks along with their meta-data. We expose functionalities that: (i) clean and restructure label tracks that are readable by softwares like Audacity, (ii) chop long recordings into small fixed-size slices, (iii) detect chunks both interactively (so that robust detection settings can be found on a subset of the recordings) and automatically (so that the detection is applied across all recordings), (iii) dispatch chunks into structured folder(s), according to their meta-data.