dataesr/scientific-paragraphs-categorization huggingface dataset:
Format of the LIBS Spectra Files Concerning data acquisition, the spectra can be directly displayed on the portable tool just after the analyses and the mean spectra are provided on the Profile Builder (internal software). These LIBS data (spectra) can be exported from the device to the user's computer by opening the communication between the LIBS and the computer (USB connection or Wi-Fi). Different options are possible such as raw LIBS spectra, mean raw spectra, or all individual and/or mean spectra calibrated in wavelength. Here, two different kinds of spectra, obtained after cleaning the laser shots to avoid any dust contamination, are available in this dataset (as downloaded from the LIBS tool): (i) Firstly, 4362 LIBS files correspond to the raw LIBS spectra with no processing treatment; thus, each spectra value corresponds to a point (corresponding to several laser shots) on the samples.
). The open access website of ORDaR (OTELo Research Data Repository) is https://doi. org/10. 24396/ORDAR-65. (ii) Secondly, 369 average LIBS spectra values calculated for each area are available. It should be highlighted that these LIBS spectra were directly recorded from the handheld LIBS tool and the spectra were calibrated in wavelength in order to facilitate element identification (as the element peaks are very narrow) by adding artificial pixels. The LIBS mean spectra values also correspond to. csv files. These calibrated spectra are the mean spectra obtained at several points, and they correspond to two columns: the first for wavelengths and the second for the intensity (arbitrary intensity, counts on the detector), with the column titles being "wavelength" and "intensity". The initial format was built as IDmineral_yearmonthday_hour_AM_AverageSpectrum.
). Multiplying the number of analyses on the rock is required in order to obtain the bulk composition  [17, 39, 56]. Crushed and homogenized samples can be investigated by making pressed pellets. LIBS analysis is slightly destructive with an ablated layer of several to dozens of micrometers per laser shot depending on the smoothness of the material. As a series of laser shots is usually emitted, the sample thickness should be a minimum of 40 micrometers (corresponding to common thin rock sections). Finely grained samples may be homogeneous enough and provide good data but there is no way of knowing if there is a hidden feature below the sample surface, shallow enough to contribute to the signal. As such, confidence in results from such samples is not at the level it would be for homogenized ones.
LIBS Spectra Acquisition (Z300 Instrument) All LIBS spectra acquisitions were obtained using one unique handheld instrument (Z300 SciAps © instruments). Depending on the samples and the size of the minerals, the number of mean spectra values acquired can be from 3 to 5 different analyzed zones corresponding to 9 and 15 points, respectively. Thus, the most relevant information regarding acquisition is available in the LIBS_Database_file. csv file.
LIBS Spectra Analysis The reading and the interpretation of the LIBS spectra can be done using the Profile Builder for the recognition of the detected element emission lines. It is possible to explore the spectra and to normalize the spectra to the total area if necessary (in the case of a change in the ablated volume, point by point), and it is possible to obtain the net intensity under the peaks to provide a calibration curve directly using the Profile Builder software after defining standards. All the detected and non-detected elements can be downloaded in a. csv format if necessary. Various different software programs can be used to treat the spectra or to establish calibration curves  [36, 43]. In the following for example, we use SpectraGryph (http://spectroscopy. ninja accessed on 1 May 2021).
Detected Elements The major elements detected by the handheld LIBS tool for all the minerals mainly corresponded to Al, Si, O, Mg, Ca, Li, Na, K, and Fe (Figure  3 ). Minor and trace elements depending on the mineral origins, such as Be, Sr, Ba, Cs, Sn, Cs, Ta, W, etc. , were also identified in numerous LIBS spectra provided in this database. The main trace elements detected in these Li-rich materials are reported in the LIBS_database_file. csv. The reference emission lines can also be obtained online from various websites and, for example, from the NIST LIBS database (https://physics. nist. gov/PhysRefData/ASD/LIBS/libsform. html accessed on 1 May 2021).
Acknowledgments: We acknowledge  Robert Linnen  for his synthetized glasses and also  Tania Martin  and  Marc Binnen  for their mineral investigation. We thank the  Suffel Collection, department of Ontario , for providing some of the Li-rich minerals. We acknowledge  SciAps Company  for technical support. Pierre-Yves Arnould  is acknowledged for his technical support for ORDaR (OTELo Research Data Repository) website.
Funding: This research was funded by the  ERA-MIN/0001/2017-LIGHTS project. The work was also supported by  Portuguese National Funds  through the  FCT-Fundação para a Ciência e a Tecnologia, I. P. , project  UIDB/04683/2020-ICT  ( Institute of Earth Sciences ). Joana Cardoso-Fernandes  and  Filipa Dias  are financially supported within the compass of their respective Ph. D. Theses, ref. SFRH/BD/136108/2018  and ref. 2020. 05534. BD, by national funds from  MCTES  through  FCT , and co-financed by the  European Social Fund (ESF)  through  POCH-Programa Operacional Capital Humano-and NORTE 2020 regional program. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Context. Chamaeleon is the southernmost low-mass star-forming complex within 200 pc from the Sun. Its stellar population has been extensively studied in the past, but the current census of the stellar content is not complete yet and deserves further investigation. Aims. We take advantage of the second data release of the Gaia space mission to expand the census of stars in Chamaeleon and to revisit the properties of the stellar populations associated to the Chamaeleon I (Cha I) and Chamaeleon II (Cha II) dark clouds. Methods. We perform a membership analysis of the sources in the Gaia catalogue over a field of 100 deg 2 encompassing the Chamaeleon clouds, and use this new census of cluster members to investigate the 6D structure of the complex. Results. We identify 188 and 41 high-probability members of the stellar populations in Cha I and Cha II, respectively, including 19 and 7 new members.
0 2. 5 5. 0 7. 5 10. 0 -10 -5 0 5 10 µ δ (mas/yr) ϖ (mas) Status q q q q rejected selected Population q q q q Cha I Cha II Fig. 1. Proper motions and parallaxes of the stars in Cha I and Cha II in the samples of  Esplin et al. (2017)  and  Alcalá et al. (2008) , respectively. Open symbols denote potential outliers in these samples based on Gaia-DR2 data identified from the MCD estimator (see Sect. 2). photometric space as a principal curve with a spread at any point along the curve given by a multivariate Gaussian (both the principal curve and its spread are initialised with a fit to the initial list of members). The method then computes membership probabilities for all sources in the field using the fraction of sources in each category (member vs. non-member) obtained in the previous iteration to estimate the marginal class probabilities.
We proceed as follows to minimise the existence of potential contaminants in our solution due to projection effects. First, we conduct an independent membership analysis following the same methodology described before but in two smaller regions centred around the Cha I and Cha II molecular clouds. Our results obtained in these regions are labelled with the term 'central region' which we use hereafter to distinguish from the membership analysis conducted over the entire Chamaeleon complex. The size of each field is defined based on the position of the candidate members previously identified in the literature with available Gaia-DR2 data that constitute our input list for the membership analysis (see Sect. 2. 1). Thus, the field for the membership analysis in Cha I covers the sky region defined by 295.
e. , 169 stars) were also included in the study conducted by  Esplin et al. (2017). However, 25 sources presented in that study could not be recovered by our membership analysis. We verified that they have proper motions and parallaxes that are either not consistent with membership in Cha I (see e. g. Figure  1 ) or result from poor astrometric solutions. For example, if we use the re-normalised unit weight error (RUWE) criterion 1  to filter the literature sources with poor astrometry (see Sect. 3. 1) we note that 17 stars among the rejected sources fail to pass this selection criterion (i. e. RUWE < 1. 4). These 17 sources can be considered at this stage as potential candidate members and future data releases of the Gaia space mission will allow us to confirm (or exclude) membership with more precise data.
Refining the sample of cluster members Let us now refine our sample of cluster members by filtering the stars with the best data available before deriving the overall kinematic properties of the Chamaeleon subgroups. To do so, we use the RUWE criterion as one possible indicator of the goodness of fit of the Gaia-DR2 astrometric solution. The value of 1. 4 is often used in the literature to distinguish between the sources with reliable (i. e. , RUWE < 1. 4) and poor astrometric solutions in the Gaia-DR2 catalogue, but some studies adopt different thresholds (see e. g. Luhman & Esplin 2020). In this paper we adopt the RUWE threshold of 1. 4 to select the sources with good astrometric solutions for consistency with the methodology adopted in other papers of this series (see e. g. Galli et al.
2020a,b). This step reduces the samples of members to 160 and 31 stars in Cha I and Cha II, respectively. The rejected stars exhibit mostly the largest uncertainties in proper motions and parallaxes as shown in Figures  4  and 5. Many of them have been identified as binaries or multiple systems in the literature (see also Figure  6 ) which explains the poor astrometry in the Gaia-DR2 catalogue. Future data releases of the Gaia space mission will deliver an improved astrometry for such systems. We searched the CDS databases  (Wenger et al. 2000)  for RV information of the stars in our sample. Our search for RVs in the literature is based on  Dubath et al. (1996) ,  Covino et al. (1997) ,  Joergens & Guenther (2001) ,  Torres et al. (2006) ,  James et al. (2006) ,  Gontcharov (2006) ,  Guenther et al.
Proper motions and parallaxes of the subgroups The stellar population of Cha I is composed of two subclusters that have been historically separated based on their position in the sky using the declination of δ = -77 • as midpoint to separate the northern and southern subgroups (see e. g. Luhman 2007 ). However, a recent study conducted by  Roccatagliata et al. (2018)  shows that the subclusters overlap and that they cannot be strictly divided by their sky positions. In this paper, we apply the Partitioning Around Medoids (PAM,  Kaufmann & Rousseeuw 1987 ) clustering algorithm to separate the two subclusters in Cha I. The PAM algorithm is an unsupervised machine-learning method for partitioning a N-dimensional dataset into k groups (i. e. clusters) that works similarly to the k-Means algorithm (MacQueen 1967), but uses medoids to represent the centers of the clusters.
We perform the clustering in the 5D space of astrometric observables (α, δ, µ α cos δ, µ δ , ) of the stars using the pam routine from the cluster package implemented in R programming language. We use the Average Silhouette  (Rousseeuw 1987)  and Gap Statistic  (Tibshirani et al. 2001 ) methods to confirm that the optimal number of clusters for our dataset is indeed k = 2, which we use as input for the clustering analysis. The clustering analysis with PAM divides the sample into 76 stars and 84 stars for Cha I (north) and Cha I (south), respectively. Figure  9  confirms that the subclusters in Cha I overlap in position, proper motion, and parallax in good agreement with the findings reported by  Roccatagliata et al. (2018). In Table  2  we compare the proper motions and parallaxes of the Chamaeleon subgroups in our sample of members.
0 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -10 -9 -8 -7 -6 -5 -24 -22 -20 -18 -16 µ α cosδ (mas/yr) µ δ (mas/yr) 0. 96 0. 97 0. 98 0. 99 Probability q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 3. 5 Probability q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q 3. 5  ence. This analysis uses the Kalkayotl package 2 (Olivares 2019;  Olivares et al. 2020 ) in python programming language, which implements a number of priors for the distance. We took advantage of this code to investigate two priors for the distance that are based on purely statistical probability density distributions (hereafter, statistical priors), and another two priors that are based on astrophysical assumptions (hereafter, astrophysical priors).
The statistical priors considered in this paper include the Uniform and Gaussian distributions. The two astrophysical priors that we use are based on the surface brightness profile of 2 The code is available at https://github. com/olivares-j/Kalkayotl. the Large Magellanic Cloud derived from star counts by  Elson et al. (1987)  and the King's profile distribution observed in globular clusters  (King 1962). More details on the implementation and performance of each prior are given in the Kalkayotl paper  (Olivares et al. 2020). The prior that we used in the case of the angular velocity (i. e. 2D tangential velocity) is a beta function following the online tutorials available in the Gaia archive  (Luri et al. 2018 ). This procedure takes as input the astrometric observables and covariance matrix of the stars as given by the Gaia-DR2 catalogue.
7 +0. 4 -0. 4 pc and 186. 5 +0. 7 -0. 7 pc derived by  Roccatagliata et al. (2018)  for the northern and southern subclusters of Cha I, respectively. However, in this case we note that our uncertainties are larger by a factor of almost two. As explained in Sect. 2 of  Roccatagliata et al. (2018)  the authors did not include the systematic errors on the Gaia-DR2 parallaxes in their analysis and derived the distance from the inverse of the parallax. In this study, we derived the distances from Bayesian inference using the Kalkayotl code  (Olivares et al. 2020)  which is designed to deal with some characteristics of the Gaia-DR2 catalogue including the parallax zero-point correction and spatial correlations which are modelled with the covariance function of  Vasiliev (2019). The existence of systematic errors in the Gaia-DR2 catalogue does not compromise the valuable astrometry delivered by the Gaia satellite, but needs to be considered to avoid underestimating uncertainties.
Hertzsprung-Russell diagram and relative ages of the subgroups In this section we use the distances derived from Gaia-DR2 parallaxes (see Sect. 3. 3) and spectroscopic data collected from previous studies to construct the most accurate Hertzsprung-Russell diagram (HRD) of the Chamaeleon star-forming region. We proceed as follows to place the stars in the HRD. First, we compile the spectral types and extinctions for individual stars determined from past studies thanks to the numerous spectroscopic surveys performed in this region. We found spectral types and extinctions for 144 stars (out of 160 stars) in Cha I and 28 stars (out of 31 stars) in Cha II which are given by  Esplin et al. (2017)  and  Spezzi et al. (2008) , respectively. Second, we compute the photospheric luminosities from the J-band given by the 2MASS catalogue  (Cutri et al.
Conclusions In this paper we revise the census of stars with available astrometry in the Gaia-DR2 catalogue that are associated to the molecular clouds of the Chamaeleon star-forming region. We applied a probabilistic method to infer membership probabilities and our analysis allowed us to identify 188 members and 41 members in Cha I and Cha II, respectively. We confirm most of the historical members from the literature (with available Gaia-DR2 astrometry) and increase the samples of cluster members by 11% and 21% in Cha I and Cha II, respectively. We combined the Gaia-DR2 astrometry with ancillary RV data from previous studies to investigate the 3D spatial distribution and 3D space motion of Chamaeleon stars. We confirm that Cha I and Cha II are located at different distances and are separated by about 23 pc in the 3D space of positions.
In this study we restricted our analysis of the stellar population of the Chamaeleon clouds to the sources with available data in the Gaia-DR2 catalogue. Our team is measuring precise proper motions of faint sources (beyond the sensitivity limit of the Gaia satellite) as part of the DANCe project and we will soon be able to expand the current census of Chamaeleon stars based on ancillary data and spectroscopic observations to accurately derive the initial mass function of this stellar group.
Acknowledgements. We thank the referee for constructive criticism that improved the manuscript. This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  682903 ,  P. I. H. Bouy ), and from the  French State  in the framework of the  "Investments for the future" Program ,  IdEx Bordeaux , reference  ANR-10-IDEX-03-02. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
45 Wild Press, P. O. Box 12-397, Wellington, New Zealand. 46 Independent researcher, 9 Roba Court, Kingston, Tasmania 7050, Australia. 47 Université de Carthage Institut National Agronomique de Tunisie, 43 Avenue Charles Nicole, 1082 Tunis, Tunisie. 48 S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 3 of 12 and competition with fisheries caused by overfishing, as well as pollution and climate-related changes to ecosystems (25, 26). Using a unique tracking dataset assembled from across the global distributions of 39 species of albatrosses and large petrels, we estimate the importance of national jurisdictions and high seas areas across the annual cycle. First, we calculate species richness for each national jurisdiction (including the dependent territories under each sovereign country) and the high seas as a whole, distinguishing between breeding and visiting species (i.
1B  and data S1). In general, countries hosting a high diversity of breeding species also hosted a high number of visiting species, and countries with larger territories had higher richness (Fig. 1B  and fig. S4 ). Brazil, Uruguay, Namibia, and Peru were each visited by ≥10 species although they have no breeding populations, and Antarctica (areas south of 60°S) was visited by 20 species (Fig. 1A  and data file S1). These species richness values were estimated directly from available tracking data; for several species, the sampled sites are not comprehensive of all breeding sites, and therefore, reported richness values are underestimates for certain countries. Breeding richness estimated from the tracking data was on average one species (mean, SD ±1. 4) fewer than true breeding richness as calculated from the literature.
DISCUSSION Migratory species cover immense distances on their seasonal movements, connecting numerous areas both within and beyond national jurisdiction and making their conservation a challenging task. By analyzing a comprehensive tracking dataset and accounting for the size of each breeding population relative to the global total, our study provides the first global estimation of the relative importance of political areas to a group of highly threatened migratory megafauna, the albatrosses and large petrels. We estimated the standalone importance of national jurisdictions and the connectivity between them, providing a potential road map for international collaboration. We also quantified the connections between breeding-origin countries and areas of legal competence of RFMOs in the high seas, providing a useful scoping tool for identifying which national populations of albatrosses and large petrels may be affected by management decisions in international fisheries.
Our results confirm that national measures for albatrosses and large petrels if coupled with coordinated, international efforts would successfully mitigate threats occurring across the ranges of these species. For example, while many species of conservation concern breeding on South African, French, United Kingdom, and Australian islands benefit from national action plans for reducing bycatch within national waters and fleets (34), birds are still at risk from longline effort in the high seas of the Indian Ocean, where they spend a considerable part of the year (Fig. 3B ). Despite this, these countries have not advocated for the improvement of bycatch mitigation measures at the IOTC since 2012. The tracking dataset used herein spanned the global ranges annual cycles for most of our focal group, the albatrosses and large petrels, providing a uniquely representative picture of their global distribution (mean coverage of 85 and 75%, respectively; Table  1 ).
Nevertheless, gaps remain in terms of untracked populations and unsampled months of the year. Using knowledge of known breeding sites and population sizes, we quantified these sampling limitations (Table  1 ) and thereby identified gaps at the species level where future work may be focused (35, 36). Tracking and population data were only analyzed for breeding adults, and therefore, our results pertain to the global breeding populations of each species in a given year. A substantial proportion of the global population is made up of juveniles, immatures, and deferring breeders (particularly for biennial species), which may have different annual distributions and hence geopolitical connectivity (37, 38). These are important gaps to fill in terms of tracking and monitoring effort; however, as these data accumulate, syntheses across ranges, taxonomic groups, and life stages can become standard practice (24).
Data assembly Tracking data were obtained from the BirdLife International Seabird Tracking Database (www. seabirdtracking. org) and directly from collaborators. Data were sought from all the breeding aggregations for each species, with the goal of maximizing coverage of the global breeding population (Table  1  and data S3 ). Each "breeding population" was defined according to criteria adopted by ACAP as individuals of each species found breeding at each "island group"; the latter correspond with major archipelagos and reflects current biogeographic and political separation (10). For each breeding population with tracking data, we maximized coverage of the annual cycle (Table  1 ) by using data from light-level geolocation (Global Location Sensor or GLS), Platform Terminal Transmitters (PTTs), and Global Positioning System (GPS) devices.