dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of Table  2  with Table  4  in  [13] , where analogous overview of the previous dataset is given, shows that the range of rotational quantum numbers was most significantly expanded in the a K quantum number (especially for the second excited torsional state 17 ).  A fit achieving a weighted root-mean-square (rms) deviation of 0. 78 for 12128 microwave and 7 FIR line frequencies with 99 parameters included in the model was chosen as our "best fit" for this paper. The 99 molecular parameters obtained from this fit are given in Table  1. Although the number of parameters in the model was significantly increased (99 here against 40 in  [13] ), our current study shows further improvement of the lines/parameter ratio in comparison with the previous results ( 122  lines/parameter here against 43  lines per adjustable parameter in  [13]  and  19   lines/parameter in  [8] [9] [10] ).
Discussion A significant increasing (almost 2. 5 times) of the number of parameters in the model in comparison with our previous work  [13]  is caused in our opinion by expanded rotational quantum number range involved in the analysis (which is especially evident for the second excited torsional state, see Table  2 ). Also, our new data provide a higher level of accuracy of measurements than that observed in the previous dataset  [13]. Indeed, in  [13] , the dominating group of data belongs to the category with 100 kHz uncertainty (see Table  4  in  [13] ), whereas in the current fit, the category with 10 kHz measurement uncertainty is dominant (see Table  2   need now a theoretical model which should be adequate at the level being by order of magnitude higher than in the previous study  [13].
In our opinion, these large changes in low order parameter values are caused by significant changes in the model and dataset, as well as by the correlation between 3 V and 33 V which is a known issue discussed in some detail by Groner and co-authors  [19, 20]. At the same time, it should be noted that in our current fitting attempts we have observed significantly narrower variation of 3 V and 33 V values among the exploratory fits to near experimental error. Therefore we think that our new experimental data gave an opportunity to put tighter constraints on the highly correlated low order torsional parameters. We hope that further experimental investigation of the acetone spectrum, especially an addition of the high-resolution data on acetone torsional bands, will allow significant reducing of the correlation between low order torsional parameters in our model, thus providing an opportunity of benchmark comparison with ab initio calculations.
Conclusions We have presented a new study of rotational spectra in the lowest three torsional states of acetone 3 3 CH COCH employing the model developed for molecules with two equivalent methyl rotors and 2v C symmetry at equilibrium that makes use of an explicit twodimensional potential function. In the current study, the dataset available in the literature was augmented by the new measurements in the millimeter wave range as well as by remeasurement of a number of transitions in the submillimeter wave range. About 10000 new line frequencies were added to the analysis. The set of 99 Hamiltonian parameters, which fits within the experimental error the dataset of 12128 microwave and 7 FIR acetone line frequencies, was obtained. The experimental dataset sample all symmetry species in 36 G of the ground, 12 1,   and 17 1   torsional states and covers the frequency range from 8 to 610 GHz.
Since the pioneering studies of the 1960s, heart rate variability (HRV) has become an increasingly used non-invasive tool for examining cardiac autonomic functions and dysfunctions in various populations and conditions. Many calculation methods have been developed to address these issues, each with their strengths and weaknesses. Although, its interpretation may remain difficult, this technique provides, from a non-invasive approach, reliable physiological information that was previously inaccessible, in many fields including death and health prediction, training and overtraining, cardiac and respiratory rehabilitation, sleep-disordered breathing, large cohort follow-ups, children's autonomic status, anesthesia, or neurophysiological studies. In this context, we developed HRVanalysis, a software to analyse HRV, used and improved for over 20 years and, thus, designed to meet laboratory requirements.
The main strength of HRVanalysis is its wide application scope. In addition to standard analysis over short and long periods of RR intervals, the software allows time-frequency analysis using wavelet transform as well as analysis of autonomic nervous system status on surrounding scored events and on preselected labeled areas. Moreover, the interface is designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in TXT files directly employable in statistical softwares. Recordings can arise from RR or EKG files of different types such as cardiofrequencemeters, holters EKG, polygraphs, and data acquisition systems. HRVanalysis can be downloaded freely from the Web page at: https://anslabtools.
Several methods for HRV quantification have been developed. They can be classified into frequency, temporal, nonlinear, and time-frequency analysis methods (Rajendra  Acharya et al. , 2006) , and they are used in a variety of research fields, as evidenced by the growing number of publications on HRV analysis (Figure  1 ). Furthermore, HRV analysis indices have a wide range of applications, including research in physiology, cardiology, sport, health, mortality prediction, sleep, and pain, as well as for routine procedures in medicine and sports. The available data for HRV analysis therefore include RR interval series obtained from Holter systems or heart rate monitors or EKG data from acquisition or polygraph systems. Analyses can focus on short-duration recordings or 24-h recordings, and the data may concern small subject samples as well as large cohorts of hundreds or thousands of subjects, as in mortality prediction studies.
Several HRV analysis methods are currently available, including Holter and heart rate monitor systems and free independent software such as Kubios  (Tarvainen et al. , 2014) , Kardia  (Perakakis et al. , 2010) , ARTiiFACT  (Kaufmann et al. , 2011) , POLYAN  (Maestri and Pinna, 1998) , RHRV  (Rodriguez-Linares et al. , 2011) , ECGLab  (Carvalho et al. , 2002) , and the HRV Toolkit developed by the PhysioNet Project  (Goldberger et al. , 2000) , each with its own specificity. Drawing on a large panel of users, we developed HRVanalysis (version 1. 0), a software that includes a wide range of HRV analysis functions, from local to 24 h-recording, from single to large cohort files, and supporting various import formats. HRVanalysis is the outcome of almost 20 years of expertise, with each feature developed and improved for optimum adaptability to a variety of settings.
, 2000). Also, interpretation of HRV indices remains especially debated in long-term recordings, because major determinants of HRV such as environmental factors, physical activity, and sleep duration vary significantly over time  (Lombardi and Stein, 2011). Thus, HRV indices can be calculated on the whole RR sample or as the mean of successive epochs. Users might be aware of that and can refer to Table  2  for these specifications. The software is optimized for analysing RR signal arizing from human recordings. R-peaks detection as well as HRV anayses can be performed on animal dataset by setting the adapted parameters in the preferences window. However, users must verify the accuracy of the methods for animals while some of them have been previously validated, such as Fourier analysis  (Cerutti et al.
Data Importation and R Peaks Detection Data can be imported from EKG or RR files. The available formats for EKG data are EDF, ISHNE, binary, text, Matlab. A generic import window allows to pre-visualize the EKG trace while the user sets the data file specifications. Preformatted RR files can be imported from text and binary files. Several preformated configurations are proposed (Suunto, Polar, Aria Delmar, and Vista Novacor Holters). R peaks are detected from EKG signals using a laboratorydeveloped algorithm largely inspired by Chen et al. 's method  (Chen et al. , 2006). This moving average-based method, initially developed for real-time QRS detection, has demonstrated a 99. 5% detection rate in the MIT-BIH Arrhythmia Database. First, the raw EKG signals are upsampled at 1000 Hz to ensure good time resolution for the RR intervals, even at a low initial EKG sampling rate.
HARDWARE SPECIFICATIONS AND SYSTEM REQUIREMENTS HRVanalysis was developed using MATLAB 2016a and compiled using MATLAB compiler 6. 2. MATLAB functions were used to develop the program, except for the calculation of entropy indices, Katz and Higuchi fractal indices, which were adapted from copyright files of Alvarez JM, the detrended fluctuation analysis, which was adapted from copyright files of Wenye G, the largest Lyapunov exponent which originates from copyright file of Wolf A, the Lampel-Ziv complexity index which comes from copyright files of Thai Q, and, the empirical mode decomposition which were adapted from copyright files of Rilling G and Flandrin P. It is not necessary to have MATLAB installed on the computer, because MATLAB Runtime v9. 0. 1 is packaged with the software, and is automatically installed if required.
AVAILABILITY, LICENSING AND INSTALLATION PROCEDURE HRVanalysis is available free of charge for non-commercial use only (Freemium license, November the 9th 2015, under the number IDDN. FR. 001. 470001. 000. S. P. 2015. 000. 30000). Interested persons can download the software from the Web page at: https://anslabtools. univ-st-etienne. fr. Registration is requested so that users may be kept informed of free software updates. The software is installed by running the Installer program and following the installation procedure. The program is delivered with a tutorial and sample runs. Bug reports, comments, and suggestions concerning the program can be emailed to ANSLabTools@univ-st-etienne. fr. When using HRVanalysis to analyze data meant for publication, please cite this article and the software download webpage in the methods section.
CONCLUSION AND FUTURE DIRECTIONS HRVanalysis was designed to meet laboratory requirements. It has been used and improved for over 20 years by the SNA-EPIS laboratory, Saint-Etienne, France. It has enabled the analysis and publication of HRV analyses for numerous purposes, including training and overtraining, cardiac and respiratory rehabilitation, sleep-disordered breathing, large cohort follow-ups, and children's autonomic status, pain (see references at https://anslabtools. univ-st-etienne. fr). The main strength of HRVanalysis is its wide application scope. In addition to standard analysis, the software allows timefrequency analysis using wavelet transform as well as analysis of autonomic nervous system status surrounding scored events and on preselected labeled areas. Moreover, HRVanalysis is suitable for a considerable range of recording modes, from single recordings to large cohorts, and including batch signal processing.
HRVanalysis is meticulously maintained and developed for inhouse laboratory use, and in response to users' comments and needs. Upcoming features to be developed in future versions include (1) more types of preformatted files for importing RR and EKG data; (2) selection of automatic RR correction levels, with specific thresholds for particular populations, such as newborns, babies, or athletes; (3) options to import events scored with other software such as polysomnography or polygraphy software, or from user-entered. txt files; (4) more time-frequency task options, such as selecting the wavelet shape and analysis level; and, (5) the addition of other significant cardiovascular signals such as blood pressure and respiration.
Alternatively, some remote places such as parts of the NW Hawaiian Islands underperform (i. e. were identified as dark spots). Detailed analysis of why bright spots can evade the fate of similar areas facing equivalent stresses will require a new research agenda gathering detailed site-level information on social and institutional conditions, technological innovations, external influences, and ecological processes  13  that are simply not available in a global-scale analysis. As a preliminary hypothesis-generating exercise to begin uncovering why bright and dark spots may diverge from expectations, we surveyed data providers and other experts about the presence or absence of 10 key social and environmental conditions at the 15 bright spots, 35 dark spots, and 14 average sites with biomass values closest to model expectations (see Methods for details).
Scales of data Our data were organized at three spatial scales: reef (n=2514), site (n=918), and nation/state (n=46). i) reef (the smallest scale, which had an average of 2. 4 surveys/transectshereafter 'reef'). ii) site (a cluster of reefs). We clustered reefs together that were within 4km of each other, and used the centroid of these clusters (hereafter 'sites') to estimate site-level social and site-level environmental covariates (Extended Data Table  1 ). To make these clusters, we first estimated the linear distance between all reefs, then used a hierarchical analysis with the complete-linkage clustering technique based on the maximum distance between reefs. We set the cut-off at 4km to select mutually exclusive sites where reefs cannot be more distant than 4km. The choice of 4km was informed by a 3-year study of the spatial movement patterns of artisanal coral reef fishers, corresponding to the highest density of fishing activities on reefs based on GPS-derived effort density maps of artisanal coral reef fishing activities  31.
Local Population Growth: We created a 100km buffer around each site and used this to calculate human population within the buffer in 2000 and 2010 based on the Socioeconomic Data and Application Centre (SEDAC) gridded population of the world database 36. Population growth was the proportional difference between the population in 2000 and 2010. We chose a 100km buffer as a reasonable range at which many key human impacts from population (e. g. , land-use and nutrients) might affect reefs  37.
Management: For each site, we determined if it was: i) unfished-whether it fell within the borders of a no-take marine reserve. We asked data providers to further classify whether the reserve had high or low levels of compliance; ii) restrictedwhether there were active restrictions on gears (e. g. bans on the use of nets, spearguns, or traps) or fishing effort (which could have included areas inside marine parks that were not necessarily no take); or iii) fished -regularly fished without effective restrictions. To determine these classifications, we used the expert opinion of the data providers, and triangulated this with a global database of marine reserve boundaries  38.
Gravity: We adapted the economic geography concept of gravity, also called interactance  39  , to examine potential interactions between reefs and: i) major urban centres/markets (defined as provincial capital cities, major population centres, landmark cities, national capitals, and ports); and ii) the nearest human settlements (Extended Data Fig. 1 ). This application of the gravity concept infers that potential interactions increase with population size, but decay exponentially with the effective distance between two points. Thus, we gathered data on both population estimates and a surrogate for distance: travel time.
Population estimations We gathered population estimates for: 1) the nearest major markets (which includes national capitals, provincial capitals, major population centres, ports, and landmark cities) using the World Cities base map from ESRI TM ; and 2) the nearest human settlement within a 500km radius using LandScan TM 2011 database. The different datasets were required because the latter is available in raster format while the former is available as point data. We chose a 500km radius from the nearest settlement as the maximum distance any non-market fishing activities for fresh reef fish are likely to occur.
Travel time calculation Travel time was computed using a cost-distance algorithm that computes the least 'cost' (in minutes) of travelling between two locations on a regular raster grid. In our case, the two locations were either: 1) the centroid of the site (i. e. reef cluster) and the nearest settlement, or 2) the centroid of the site and the major market. The cost (i. e. time) of travelling between the two locations was determined by using a raster grid of land cover and road networks with the cells containing values that represent the time required to travel across them  40  (Extended Data Table  5 ), we termed this raster grid a friction-surface (with the time required to travel across different types of surfaces analogous to different levels of friction). To develop the friction-surface, we used global datasets of road networks, land cover, and shorelines: -Road network data was extracted from the Vector Map Level 0 (VMap0) from the National Imagery and Mapping Agency's (NIMA) Digital Chart of the World (DCW®).
We converted vector data from VMap0 to 1km resolution raster. -Land cover data were extracted from the Global Land Cover 2000 41. -To define the shorelines, we used the GSHHS (Global Self-consistent, Hierarchical, High-resolution Shoreline) database version 2. 2. 2. These three friction components (road networks, land cover, and water bodies) were combined into a single friction surface with a Behrmann map projection. We calculated our cost-distance models in R 42 using the accCost function of the 'gdistance' package. The function uses Dijkstra's algorithm to calculate least-cost distance between two cells on the grid and the associated distance taking into account obstacles and the local friction of the landscape  43. Travel time estimates over a particular surface could be affected by the infrastructure (e.
As is standard practice in many agricultural economics studies  45  , an assumption in our study is that the nearest major capital or landmark city represents a market. Ideally we would have used a global database of all local and regional markets for coral reef fish, but this type of database is not available at a global scale. As a sensitivity analysis to help justify our assumption that capital and landmark cities were a reasonable proxy for reef fish markets, we tested a series of candidate models that predicted biomass based on: 1) cumulative gravity of all cities within 500km; 2) gravity of the nearest city; 3) travel time to the nearest city; 4) population of the nearest city; 5) gravity to the nearest human population above 40 people/km 2 (assumed to be a small peri-urban area and potential local market); 6) the travel time between the reef and a small peri-urban area; 7) the population size of the small peri-urban population; 8) gravity to the nearest human population above 75 people/km 2 (assumed to be a large peri-urban area and potential market); 9) the travel time between the reef and this large peri-urban population; 10) the population size of this large peri-urban population; and 11) the total population size within a 500km radius.
Tourism: We examined tourist arrivals relative to the nation/state population size (above). Tourism arrivals were gathered primarily from the World Tourism Organization's Compendium of Tourism Statistics. 7. National Reef Fish Landings: Catch data were obtained from the Sea Around Us Project (SAUP) catch database (www. seaaroundus. org), except for Florida, which was not reported separately in the database. We identified 200 reef fish species and taxon groups in the SAUP catch database  47. Note that reef-associated pelagics such as scombrids and carangids normally form part of reef fish catches. However, we chose not to include these species because they are also targeted and caught in large amounts by large-scale, non-reef operations.
Productivity: We examined ocean productivity for each of our sites in mg C / m2 / day (http://www. science. oregonstate. edu/ocean. productivity/). Using the monthly data for years 2005 to 2010 (in hdf format), we imported and converted those data into ArcGIS. We then calculated yearly average and finally an average for all these years. We used a 100km buffer around each of our sites and examined the average productivity within that radius. Note that ocean productivity estimates are less accurate for nearshore environments, but we used the best available data.
Analysing conditions at bright spots For our preliminary investigation of why bright and dark spots may diverge from expectations, we surveyed data providers and other experts about key social, institutional, and environmental conditions at the 15 bright spots, 35 dark spots, and 14 sites that performed most closely to model specifications. Specifically, we developed an online survey using Survey Monkey TM software, which we asked data providers who sampled those sites to complete with input from local experts where necessary. Data providers generally filled in the survey in consultation with nationally-based field team members who had detailed local knowledge of the socioeconomic and environmental conditions at each of the sites. Research on bright spots in agricultural development  13  highlights several types of social and environmental conditions that may lead to bright spots, which we adapted and developed proxies for as the basis of our survey into why our bright and dark spots may diverge from expectations.
2. 3 To extract the components descriptions, the IEG applied text-analytic methods to all PADs for the period 1997-2012. The project-description annex was extracted from each PAD. The contents of the annex were scanned for headers describing the components. Where these could be identified, the components were parsed out and numbered sequentially. For some of the cases, components could not be distinguished, and the project was considered to have a single component. The extracted components were stored in an Access database, which included descriptors of the the associated project such as beginning and end date, sector, and country.
We have used the Euclidean length of edges in the layout as a criterion to filter out additional edges, keeping about 95% of them. Those longer edges connect groups laid out further away from each other. We have then applied a clustering algorithm  (Blondel and others 2008)  using the link structure to identify groups. The Tulip database that accompanies this paper contains a pull down menu that allows screen by screen visualization of each of the derived clusters. 2. 10 The graph can be arranged temporally. The Tulip visualization arranges links so that prior-approved projects are on the left. Figure  4  shows an example. The nodes (projects) can be color coded according to region, sector, or outcome rating. The width of the edges (links) corresponds to the strength of similarity. The links can be coded according to the sector, region, or rating of the source ('parent') or target ('child') component.
Construction of 'influence' measures 2. 11 For ease of terminology, we will say that component A 'influences' component B if they are similar in content and if A's approval year was prior to B's. (We recognize that causal impact, or direct diffusion of ideas, cannot be verified without additional information. ) Many network-theoretic measures are available to quantify the strength of 'influence'. One could, for instance, look at connections that go from 'parents' to 'children' to 'grandchildren'. Because World Bank projects last about eight years, the number of 'generations' in our database is limited. Therefore we start with a simple measure of 'influence': the weighted sum of outlinks from a component. Project 'influence' can be computed simply as the sum of the 'influence' weights of the project's components.