dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative measures of gray matter volume were extracted within the basal ganglia brain structures, the left and right caudate and putamen, as labeled in AAL in every participant. This way of engineering morphological brain features yielded as many volumetric brain variables per patient as the total number of voxels included in these AAL regions (i. e. , 10,881). All region-wise structural volumes were transformed into z-scores by mean centering and unit-variance scaling. The first 10 principal components of the standardized basal ganglia volumetric brain measures as well as the first 10 principal components of the standardized clinical and anamnestic data were fed into a CCA. Following the preceding whole-brain CCA (cf. previous paragraph), only the first mode was automatically computed.
Acknowledgements The study was supported by the  DFG  (Grant number:  410314797 ). NC was supported by the  Clinical Scientist Program (rotation program ) of the medical faculty  RWTH, Aachen  ( 2015-2017 ). DB was supported by the  Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund) ,  Google  ( Research Award ), and by the  CIFAR Artificial Intelligence Chairs program  ( Canada Institute for Advanced Research).
Code availability Python was selected as the scientific computing engine, the open-source ecosystem of which helps enhance replicability, reusability and provenance tracking. Scikit-learn  60  provided efficient, unit-tested implementations of state-of-the-art statistical learning algorithms (http:// scikit-learn. org). All analysis scripts of the present study are readily accessible to the reader online (https:// github. com/ JLefo rtBes nard/ RLS_ 2019).
Sampling and isolating Saccharomyces cerevisiae From September 2016 to October 2020, we collected a total of 2,461 environmental samples from various substrates (bark n=340, twigs n=328, leaf n=528, litter n=320 and fruit n=78) surrounding 693 plant hosts (Supplementary Table  2 ). A total of 339 lichen samples, aliquots from six fermentation practices, and 68 from other sources (insect corpse n=43, fruiting body n=14, industrial strains n=5 and others where biomaterial was sampled only once n=6) were also collected. Collection time and GPS coordinate in gpx format of host plants was recorded on the day of collection. Leaves, flowers of host plants were photographed. Bioclimatic variables of sampling sites were retrieved from CHELSA 67 database (v1. 2) using recorded GPS coordinates. Digital terrain models (DTM) of sampling sites were retrieved from Taiwan's Open Government Data website (https://data.
gov. tw/dataset/35430). Environmental samples were collected using alcohol sterilized tweezers or spoons and stored in zip bags. Whenever possible during the sampling trips metadata such as the identity of the host plant, lichens and altitude were recorded. Samples were redistributed into 50ml falcon tubes and stored at room temperature. Each sample was divided into two proportions and immersed in two enrichment media: a liquid medium made up of either i) 3 g/L yeast extract, 3 g/L malt extract, 5 g/L peptone, 10 g/L sucrose, 7. 6% EtOH, 1 mg/L chloramphenicol, and 0. 1 % of 1-M HCl as used in ref  12  or ii) YPD containing 10% dextrose and 5% ethanol adjusted to pH 5. 3 as used in ref  68. Samples were incubated at 30℃ until signs of microbial growth and fermentation were detected, such as white sediment and effervescence.
Sediments were then streaked onto YPD agar plates. Single colonies were picked out and incubated in potassium acetate medium 23°C for 7-10 days  69. Single colonies with ascus-like (four spores) structures under microscope were picked out and streaked onto YPD agar plates. Sanger sequencing and gel electrophoresis of ITS1-5. 8S-ITS2 region PCR amplified with ITS1F/ITS4 primer set were performed to identify the species of isolates  70, 71. Pilot sampling, modification and rationale during the course of sampling strategies are further provided in Supplementary Info. Sampling efforts were visualized using the R's package ggplot2 (v. 3. 3. 5) and annotated with metR (v. 0. 10. 0; https://github. com/eliocamp/metR) and ggspatial (v. 1. 1. 5; https://paleolimbot. github. io/ggspatial/). In order to determine ploidy levels for our isolates, we carried out flow cytometry analysis for the 105 Taiwanese isolates from this study using propidium iodide (PI) staining assay using previously established protocols  72  (Supplementary Info).
Finished DNA libraries were sequenced on the Illumina Miseq platform using 2x300 bp pair-end sequencing chemistry. Raw sequencing reads containing the Illumina sequencing index were demultiplexed using sabre (v1. 0; https://github. com/najoshi/sabre). Sequencing quality was determined using FastQC (v0. 11. 7; https://github. com/sandrews/FastQC). Reads were quality filtered based on a Qscore >20 and 50 base pairs were trimmed from the 3'end end using usearch 76 (v11. 0. 667). Filtered reads were processed following the UPARSE 77 pipeline. In brief, paired reads were merged and dereplicated into unique sequences. Unique sequences were filtered using usearch default settings. Filtered sequences were denoised into zero-radius operational taxonomic unit (zOTUs) using the unoise2 78 algorithm. The taxonomy of zOTUs was classified using the SINTAX 79 algorithm (Edgar 2016) against the UNITE  80  Fungal database (v8.
Variant calling To determine the evolutionary history of new Taiwanese isolates, we collected a total of 219 published genomes representing established S. cerevisiae industrial and natural populations: 102 isolates from the 1,011 genome project   33  and eight isolates from olive brine  34. This combined with the 121 isolates from this study yielded a total of 340 individuals, 30% of which originated from industrial sources and 70% from the natural environment (Supplementary Table  6 ). Read quality was examined with FastQC (v. 0. 11. 9; https://github. com/sandrews/FastQC). Read quality and adaptor trimming was performed using.
98 14. 1,optionsk-mersize21,33,55,77 and --careful). Consensus sequences of the assemblies were polished with four rounds of Racon90 (v1. 4. 11), one round of Medaka (v1. 0. 1) using nanopore raw reads and five rounds of Pilon using Illumina reads. The assemblies were further scaffolded using RagTag 91 against the S288C genome reference. Annotations were then transferred using Liftoff92, with additional de novo annotations using Augustus 93 on regions without any transferred annotations. The assembly metrics and description of the nanopore assemblies are shown in and YPS138 from https://yjx1217. github. io/Yeast_PacBio_2016/data/. Orthology considering synteny information was inferred using PoFF98.
Acknowledgement We thank  Cheng-Ruei Lee  for the insightful comments on the ADMIXTURE analyses. We thank  Mao-Ning Tuaumu  for the helpful suggestions on how to deal with bioclimatic variable data. We thank  Cheng-Ruei Lee ,  Jun-Yi Leu ,  Dang Liu  and  John Wang  for commenting on earlier versions of the manuscript. We thank  Jun-Yi Leu  for the experimental advice and  Bo-Fei Chen  for helping with the initial sampling trips. We thank  Tze-Fu Hsu ,  Yi-Hsiu Kuan ,  H. Thorsten Lumbsch ,  Matthew Nelsen , for collecting/providing some of the biomaterials. We are grateful to the  National Center for High-Performance Computing  for its computer time and for letting us use its facilities. I. J. T. was supported by the  Ministry of Science and Technology, Taiwan  under grant  110-2628-B-001-027  and  Career Development Award   AS-CDA-107-L01 ,  Academia Sinica.
98 14. 1,optionsk-mersize21,33,55,77 and --careful). Consensus sequences of the assemblies were polished with four rounds of Racon90 (v1. 4. 11), one round of Medaka (v1. 0. 1) using nanopore raw reads and five rounds of Pilon using Illumina reads. The assemblies were further scaffolded using RagTag 91 against the S288C genome reference. Annotations were then transferred using Liftoff92, with additional de novo annotations using Augustus 93 on regions without any transferred annotations. The assembly metrics and description of the nanopore assemblies are shown in and YPS138 from https://yjx1217. github. io/Yeast_PacBio_2016/data/. Orthology considering synteny information was inferred using PoFF98 Supplementary Table 8. Phylogenomic analyses After removing 43,695 invariant sites resulting from ambiguous nucleotide codes 97,95 all isolates, the remaining 765,169 variable sites were used to construct a phylogeny for the 340 isolates.
The resulting best-fit model was indicated by BIC to be TVMe+R3 first with IQ-TREE. In addition, a maximum likelihood phylogeny was inferred using IQ-TREE with the TVMe+R3+ASC model and a 1,000 ultrafast bootstrap approximation94,95. To infer the Saccharomyces cerevisiae lineage phylogeny, amino acid, nucleotide sequences and annotation of proteomes from the following species in the Saccharomyces sensu stricto clade were downloaded: S. bayanus FM1318 (Genbank accession GCA_001298625), S. bayanus CBS7001 (Genbank accession GCA_000166995. 1), S. kudriavzevii NBRC 1802 and ZP 591 from ref96, S. jurei from ref97, S. arboricolus H6, S. paradoxus CBS432, N44, UFRJ50816, UWOPS91-917. 1.
Because machine learning procedures would perform this classification task relying on inference and a large panel of descriptors of boat trajectory patterns, they are expected to provide a more effective and generalizable analytical framework of boat movements in small-scale fisheries, as they have done so already in large-scale fisheries  (de Souza et al. , 2016). In this paper, we investigate the performance of a random forest algorithm and a speed threshold method for automatically classifying boat movements into fishing and non-fishing activities in a small-scale reef fishery in Madagascar. For this case study we monitored fishing boat movements using GPS trackers and ground-truth GPS data across five fishing gear types. Fishing boat trajectories were then processed per gear type through the random forest model and a speed threshold method following a comparative approach.
Study site and tracking data collection The study area was located in the bay of Toliara, southwestern Madagascar (Fig. 1 ). Owing to the proximity of the city of Toliara (326,000 inhabitants in 2018), this 157 km 2 -wide reef and lagoon complex has been intensively exploited to feed the growing urban demand in fish  (Bruggemann et al. , 2012). We registered a total of 892 2. 5-7 m long, outrigger sailing canoes targeting reef fish. Those traditional fishing boats were operated by one to five fishers using the typical gear types of coastal fisheries in the Western Indian Ocean region  (Bruggemann et al. , 2012). We monitored the movements of a sample of 31 boats using small GPS trackers (i-GotU GT600 and Catlog) in 2017 and 2018. We provided an explicit explanation of future information use and a small financial Fig.
1. Location of the survey site in southwestern Madagascar (A). Grey area: land mask (see Section 2). The small-scale fishery is operated by hundreds of traditional sailing boats (B) whose sail was usually folded while fishing (C). incentive (USD 0. 1. day -  ) to participant fishers. The GPS tracker recorded boat position at around 45 s time intervals to detect potential rapid change in fishers' activities during eight to fifteen consecutive days, according to battery life. A total of 306 daily tracks were recorded using the five major gear types of the fishery: beach seine, mosquito trawl net, gillnet, handline, and speargun (Table  1 ). To ground-truth GPS location data (e. g. Alvard et al. , 2015) , fishers' behaviour was simultaneously recorded by a single on-board observer for 15 and 49 of these boats and tracks, respectively (Table  1 ).
The observer recorded the nature, time, and duration of all fishing and nonfishing activities in a logbook using a watch synchronized with GPS time. He then combined this data with the corresponding GPS tracks to determine typical fishing trajectory patterns per gear type (see Section 3. 1). Such typical, gear-specific track patterns showed that GPS data was acquired at appropriate time resolution for monitoring the fishers' behaviour. They further allowed the observer to cautiously pre-classify the 257 unseen GPS tracks into fishing and non-fishing activities at a high accuracy using a geographical information system (GIS), which validated the ground-truth approach and the analytical outputs of this survey. Travel between landing sites and fishing spots was also categorized for each track.
Analysis via speed threshold method Boat GPS positions of each recorded track were classified into fishing and non-fishing events using a speed threshold. Since preliminary information on boat's speed was not available in the fishery surveyed, this threshold was estimated using our empirical data. Boat speed was calculated at each GPS position by dividing the distance by the time interval between two consecutive boat positions (i. e. 60 s). Using the whole dataset, the speed threshold was then determined as that speed that corresponded to the best trade-off between sensitivity and specificity, defined as the rate of true positives (i. e. proportion of fishing events that were correctly predicted as such) and true negatives (i. e. proportion of non-fishing events that were correctly predicted as such), respectively.
Analysis via random forest algorithm The 306 pre-classified fishing tracks were used as a ground-truthed GPS dataset to train a random forest algorithm in a cross-validation setting  (Boehmke and Greenwell, 2019). A total of 24 covariates were calculated at each boat GPS position to account for the local track geometry. Those covariates described i) the area of the polygon obtained by connecting 5-10 consecutive positions (Conv), ii) the latter covariates divided by the square of the polygons' perimeter (ConvP), iii) the number of GPS positions within a circle of a 20-100 m radius (Circle), iv) the angle between three consecutive positions (Angle), and v) the average speed between 5 and 10 consecutive positions (Speed) (Table  2 ). The choice of these covariates was derived from the boat.
These covariates were then used as features in a random forest algorithm. The random forest hyperparameters were optimized as follows: the Gini impurity index was used as the node splitting criteria while keeping at least 10 GPS positions at each node of the decision trees; the number of trees (200, 500, 1000, and 1500) and the random subsets of covariates to possibly split at each node  (2, 4, 6, 8, 10, and 12)  were determined to maximize model performance using the area-under-the ROC curve (AUC). A cross-validation procedure was followed to prevent over-fitting of the model. For each gear type, that procedure consisted of i) selecting the pre-classified tracks of all except one fisher as a learning dataset, ii) training a random forest model and using that model to predict the probability of a fishing event at each boat GPS position of the remaining fisher's tracks, iii) repeating the previous step so as to perform model prediction for each fisher, and iv) producing a global confusion matrix of model predictions.
The mean difference in fishing effort between observer's measures and predictions per gear type was calculated and compared between methods using kernel density plots. Data analysis was performed using R caret  (Kuhn et al. , 2018)  and ranger  (Wright and Ziegler, 2017)  packages. A sample of track data and a methodological brief of the R code is available at https://doi. org/10. 23708/IBZJGD.
Performance and outputs of analytical methods The predictions of both the boat speed threshold method and the random forest model had high to very high performance criteria scores for four gear types (gillnet, speargun, handline, and mosquito trawl net), though in most cases the random forest model performed better (Table  1 ). However, results highlighted that low-speed and high-speed movements of beach seine fishers were far from systematically associated with fishing and non-fishing events, respectively, making boat speed a poor predictor of fishing activity for that gear type. There was no evidence of linkage between the random forest model's performance and sample size across gear types (Table  1 ), suggesting that the dataset was large enough across each gear type for the random forest model to be effective in our case study.
Acknowledgements This study was conducted as part of the  IH. SM-IRD research partnership program  about  Aquaculture and management of Coral ecosystems in Madagascar (JEAI ACOM ). Funding was provided by the  Laboratory of Excellence "Corail"  ( GEOFIA  project, grant N •  2AO2017 ), France and the  Critical Ecosystem Partnership Fund (CEPF-IH. SM, grant  N • 66341 ). We thank the participant fishers who actively contributed to the implementation of this survey. We thank  Abdoulrahman Farah ,  Jean-Eudes Hollebecq , and  Mathis Rouzoul  for their exploration of machine learning models. We are grateful to  Elizabeth Hodson  for proofreading the original manuscript. We also would like to thank the anonymous reviewers who suggested significant improvements to the original manuscript.
unreliable). All fixation proportions were transformed via the arcsin square function to better fit the assumptions of the t test. Adjacent time points with a t value greater than a predefined threshold (t = 2, as in  Dautriche et al. , 2015)  are grouped together into clusters. We then define the size of each cluster as the sum of the t values from each of its constituent time points. Second, for each cluster, we assess the probability of observing a cluster of that size by chance through permutation. To do this we conduct 1,000 simulations where we randomly shuffle the relevant experimental conditions (i. e. , reliable vs. unreliable) for each participant, while holding constant all other aspects of the data's structure. For each permuted data set, we then identify clusters using exactly the same procedure as above, and reserve the largest of these clusters, eventually creating a distribution of largest clusters that were generated under this null (permuted) hypothesis.
In order to explore this possibility we set the exponential distribution as the basic template function for modeling the distribution of t IET :                      IET IET GWS GWS GWS 1 | exp t f t x x x (3) and the possible dependencies on hydrological data (in this case x GWS ) are modeled writing the μ parameter of the exponential distribution in terms of deterministic functions of x TWS of the explanatory covariates. x GWS is measured with respect to a reference GWS res assumed to be the minimum value reached by this parameter in the analyzed period. We hypothesize that seismicity rates observed in this zone may be the result of the superposition of a constant rate due to tectonic loading and a seismicity rate perturbation caused by hydrologically driven stress changes.
Acknowledgments We thank  L. Anderlini  for helpful discussions and suggestions on stress analysis and  E. Scoccimarro  for suggestions on the analysis of pluviometric data. This work and  F. Pintori  are supported by the project  TRANSIENTI , founded by the  Italian Ministry of Education, Universities and Research (MIUR)  " Premiale 2014. "L. Longuevergne acknowledges the support of  SNO H+ and OZCAR Research Infrastructure.
Data Availability Statement Precipitation, temperature, and river flow data are provided by "Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto" (ARPAV): https://www. arpa. veneto. it/bollettini/storico/Mappa_2019_ TEMP. htm. Extraterrestrial irradiance data are available from http://www. soda-pro. com/web-services/ radiation/extraterrestrial-irradiance-and-toa. Drainage direction maps used to define river basins are available on www. hydrosheds. org/page/availability. The analyzed seismic catalog is available in the supplementary material of  Romano et al. (2019). We use publicly available raw GNSS data. However, RINEX data can be requested from authors, if not yet available on the original repositories. Raw GPS time series are available on https://doi. org/10. 1594/PANGAEA.
912895. The Collalto Seismic Network data are available on https://doi. org/10. 7914/SN/EV. GRACE EWT data: D. N. Wiese, D. -N. Yuan, C. Boening, F. W. Landerer, M. M. Watkins. 2019. JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height JPL Release 06 Version 02. Ver. 2. PO. DAAC, CA, USA. Data set accessed [2020-04-29] at https://doi. org/10. 5067/TEMSC-3MJ62. The MATLAB code for GWS estimation is available from http://dx. doi. org/10. 17632/m5p5xmrr7k. 1. The MATLAB code for vbICA decomposition is available from http://dx. doi. org/10. 17632/n92vwbg8zt. 1.
In situ measurements Physical and biological variables (horizontal velocities, temperature, salinity and abundances of the different phytoplankton functional groups) were measured at high frequency all along the route. In situ systems included a SeaSoar deployed at sea, a vessel-mounted acoustic Doppler current profiler (VMADCP), a thermosalinograph (TSG) and a flow cytometer installed on board. The SeaSoar is a towed undulating vehicle capable of achieving undulations from the surface down to 400 m. Two Sea-Bird SBE-9 (with SBE- tivity) and pressure. The conductivity and temperature data were lag-corrected to reduce salinity spiking following the methodology developed by  Lueck and Picklo (1990) ,  Morison et al. (1994)  and  Mensah et al. (2009) , before conversion to absolute salinity S A and conservative temperature , in accordance with TEOS-10 standards  (McDougall et al.
, 2012). Herein, temperature and salinity refer to absolute salinity and conservative temperature. Horizontal velocities between 19 and 253 m were measured with a VMADCP operating at 150 kHz. VMADCP data treatment was performed with the MATLAB software Cascade V. 7 (https://www. umr-lops. fr/ en/Technology/Software/CASCADE-7. 2, last access: 3 December 2021). The sea surface temperature and salinity were measured continuously along the ship route by the underway TSG. The TSG was equipped with two sensors: (i) a CTD Sea-Bird Electronics SBE 45 sensor, which was installed in the wet lab, was connected to the surface water and which continuously pumped seawater at 3 m depth; and (ii) an SBE 38 temperature sensor, which was installed at the entry of the water intake. An automated CytoSense flow cytometer (CytoBuoy b.
Data Description The database mainly includes laboratory LIBS spectra obtained from homogeneous Li-bearing minerals such as spodumene (LiAlSi2O6), petalite (LiAlSi4O10), amblygonite or montebrasite (LiAl(PO4)(F,OH)), lepidolite (K2(Li,Al)5-6(Si6-7Al2-1O20)(OH,F)4), zinnwaldite (KLiFeAl(AlSi3)O10(OH,F)2), and altered Li minerals  [53]. Various LIBS analyses were also directly performed on Li-rich pegmatites at fresh outcrops in the field in Portugal by our Portuguese colleagues  [46, 54, 55]. LIBS analyses were additionally performed on different powder pellets (obtained from the FAME project) and glasses made up of crushed powders from homogenized minerals. These samples (pellets and glasses) were specially tested to check the potential matrix effect that occurs during plasma formation and its emission.
In this study, 134 samples were analyzed using the handheld LIBS tool, covering the main minerals investigated during the LIGHTS project. In order to help the users of our dataset, the Li concentrations of minerals, calculated using bulk analysis such as ICP-OES, are available on the LIBS_database_file. csv with other important information, such as (i) the name of sample/spectra; (ii) sample matrix: mineral/rock, pressed pellet, synthetic glass; (iii) mineralogy if known; (iv) main trace elements detected by LIBS; (v) number of point analysis (mean number of available spectra) by sample; (vi) Li concentration (%). A first description of the samples is reported in the Table  1. According to the rock mineralogy, the number of LIBS spectra acquired in each area (higher than 45) is sufficient to provide consistent data that reveal the mean composition of the rock.