dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We construct models that estimate well-being measures in the United States using a very large amount of search engine data covering the years 2008-2013. Rather than using search volumes for hundreds of words, data are condensed into several "composite category" variables that can be interpreted as different life dimensions (such as family life or financial stress). Using these composites we built two models that fit the Gallup survey trends in subjective well-being at the aggregate US level (forming the 'US-level model') as well as at the state level (yielding the 'state-level model'). Both models display high out of sample correlation. We run a simple variance decomposition to quantify the contributions of each dimension to the estimated wellbeing. We also compare selected predictors across models, and find that, in particular, searches related to Job Search, Civic Engagement, and Healthy Habits have high predictive power for well-being across models and samples.
Estimating subjective well-being in the United States and reliability of the model Both US-level and state-level models are reliable in out of sample tests, they yield fairly consistent predictions when controlling for seasonal trends at the quarterly or at the monthly frequencies, and they perform better than a 'benchmark model' that uses only seasonal predictors. The procedural framework for developing the estimation is consistent at the state and national levels. Table  6  displays the correlations between the estimated and actual values of subjective wellbeing variables for both the US-level and state-level models as well as for the benchmark model used for comparison, over both the training and the test sub-periods. The US-level model uses categories and monthly dummies at the aggregate US level with one observation per week as presented in Tables  1  and 2 ; the state-level model uses categories and biannual data with fifty observations per biannual period and is presented in Tables  4  and 5 ; the For the US-level model, during the training sub-period, correlations between the estimated and actual series are high, as expected, with an average of 0.
Health and Wellness Healthy Habits The state-level model yields estimates with similarly high correlations with actual data in and out of sample, on average, and Fig  3  shows the scatterplots of the estimated and observed state-level biannual subjective well-being indicators. The US-level model and the state-level models both perform much better than the benchmark model, indicating that the Google Trends data add information to the seasonality observed in the series. The benchmark model performs relatively poorly even in the training set, with an average correlation of 0. 45. 0. 239 � � � 0. 325 � � � 0. 395 � � � 0. 335 � � � 0. 216 � � 0. 449 � � � 0. 198 � � � 0. 240 � � � 0. 219 � � � (0. 083) (0. 068) (0. 064) (0. 086) (0. 097) (0. 084) (0. 060) (0. 078) (0. 064) Summer Leisure Health Conditions 0.
Gallup data As described previously in Algan et al. [38] , the subjective well-being data is taken from Gallup Analytics, which is a daily telephone survey of at least 500 Americans aged 18 and older. The time span of the each of the ten series used in this paper covers 300 weeks from January 6, 2008 to January 4, 2014. More than 175,000 respondents are interviewed each year, and over 2 million interviews have been conducted to date since the start of the survey in 2008. The survey includes 6 measures of self-reported positive emotions (happiness, learn, life evaluation today and in 5 years, laugh, being respected) as well as 4 measures of negative emotions (anger,.
Google search data This paper uses data on the volume of internet searches for individual words, which are available from Google Trends (see Algan et al. [38] ). The initial list of words is selected as follows. We extract two long lists of words potentially linked to subjective well-being outcomes. The first list comes from the Better Life Index Online Database, which records answers from data users to the question "What does a Better Life mean to you?" The second list is based on the American Time Use Survey, which records the daily activities undertaken by US citizens as well as the positive or negative emotions that are associated with these episodes. This selection method allows us to avoid a cherry picking of a limited set of search queries on Google. On the other hand, survey-based words may be disconnected from the day-to-day life of Americans if they do not include their usual internet queries or do not reflect their practical living conditions.
��� p<0. 01 �� p<0. 05 � p<0. 1. https://doi. org/10. 1371/journal. pone. 0209562. t007 different life experiences related to subjective well-being, for example, job concerns (e. g. 'unemployment'), poverty ('coupons') or family stress ('women shelter'). In total, the initial database contains 827 words, of which 201 were related to material conditions (income, wealth, employment, and housing) 529 were related to quality of life (health, leisure, education, environment, civic life, personal security, subjective well-being, and social connectedness) and 97 were related to potentially taboo categories (pornography, hatred and racism, and conspiracy theories). The words that were used are likely to be highly specific to the United States, and potentially specific to the time period studied. Data for each of the ten well-being variables from Gallup and the search volumes from Google Trends are available at different time intervals for the United States at the aggregate level, and we have used data at the weekly level in both cases, for the period 2008-2013.
State panel data We have also obtained search volumes for the words from Google Trends for each of the 50 states, when available, at a monthly frequency. While Gallup data are collected from states, sample sizes are not large enough to produce high-frequency representative estimates. As a result, Gallup provides state-level data only at a biannual frequency, which consists of the aggregation of multiple waves from the high-frequency national sample. In some cases, the sample sizes for Gallup data are too low, and so we exclude all observations with sample size below 1500. We take six-month averages of the composite categories (discussed below) at the state level, and match this with the biannual state subjective well-being measures from Gallup to create a panel dataset with 597 observations.
Challenges with the google trends data As described previously in Algan et al. [38] , the search volumes for individual words obtained from Google Trends pose several challenges for estimation. The Google Trends data on search volume is not the raw search volume; rather it is the proportion of total searches over a given period that included that keyword, normalized so that the highest volume over the period is equal to 100. This has several consequences: first, the value of the series obtained directly from Google Trends is difficult to interpret, as it depends not only on the volume of searches for a given word but also on the volume of other searches. Second, the value of the series on any given day cannot be compared between terms, since they are normalized to the maximum value by term.
Mass layoff data Federal and state Worker Adjustment and Retraining Notice (WARN) Acts require businesses above a certain size to give advance notice of layoffs over a certain threshold of number of workers (the threshold depends on the state, but any business anticipating a layoff of 1000 people would be required to give notice). We obtained data on large-scale layoff notices (over 1000 employees) from 2008-2013 for sixteen states (those which made data on WARN notices downloadable for this period). The data that support the findings of this study are available from Gallup but restrictions apply to their availability as access to these data was purchased for the current study. The raw data from Google Trends is freely available for download, however the terms of use prohibit distribution of these data without permission.
However, we used the raw data (without the removal of seasonal variations) in order to construct the categories. The grouping took place at the national level using the national dataset. We grouped the words into categories (such as jobs or family), then ran a factor analysis within each category. Words were excluded if the factor loading was negative or less than 0. 3, and many words were not used because they did not fit consistently with any category grouping (in a handful of cases terms with slightly lower factor loadings were retained because the correlation with other words in the group was high for most of the period but they did not share one shock, such as home alarm and mugging which share most patterns with other words in the Personal Security category but do not share the shock of the Sandy Hook murders).
Test and training periods We then build a model using these categories to estimate well-being, training the model on the Gallup data, using either aggregate US data at the weekly level or the panel state data at the biannual level. Regarding model selection and out-of-sample estimation, we divide the sample into a "training" and "test" sample, and use the training sample to build the model, and evaluate its performance on the test sample. Data from the beginning of 2009 through the end of 2012 is used for the training set (200 weeks in the US data and 406 observations in the state dataset), while data from 2008 is used for one test set and data from 2013 for the other (a total of 100 weeks in the US dataset and about 100 observation for each year for the state dataset). The test sample brackets the training data, but does not overlap.
Selection of predictor categories Our goal is to build a model that estimates the evolution of well-being using data from Google Trends. Such a model must avoid overfitting and underfitting the training data. Overfitting (as would be the case if all available individual words and month dummies were used) would result in high explanatory power in sample, with R 2 statistics near one, but low predictive power out of sample, because the model would be calibrated to fit random variations in the sample rather than actual relationships. Conversely, using too few predictors creates a risk of underfitting of the dependent variable, which also would yield poor estimates. This dual problem is pervasive in the world of 'Big Data', which is often characterized by the availability of a lot of information (i.
We repeat this exercise at the state level using the state level panel dataset at the biannual frequency. The same stepwise procedure as above is applied for all states, red states alone, and blue states alone (Tables  3  and 4 ). A stepwise procedure is used to select the explanatory variables, controlling for state fixed effects, and the model is obtained from the resulting OLS regression. Comparing performance of these models with a Newey-West estimator yield quite similar models and do not give significant improvement in the out of sample correlation, suggesting that the potential autocorrelation of residuals does not impair the performance of models estimated with robust standards errors.
Mass layoff announcements and well-being We estimate the change in well-being following the announcement of a layoff in the 16 states for which we have data. The structure of these data both demand and allow for a more flexible specification than the examination of well-being in red states following Obama's election. As before, we use the same difference-in-difference setup, but also account to potential effects linked to the time distance to a mass layoff announcement. In particular, this refined setup allows for the fact that a mass layoff could partly be anticipated by people before its official announcement (due to media coverage), and for the fact that it could have persisting effects after the month of announcement. In practice, we use the classical framework developed by Clark et al.
Acknowledgments The authors gratefully acknowledge  Florian Guyot  for contributions at an earlier stage of the paper;  Daniel Cohen ,  Martine Durand ,  Serguei Guriev ,  Marco Mira d'Ercole  and  Paul Schreyer  for helpful comments and suggestions, as well as the seminar participants at the OECD. This document expresses the views of the authors and does not necessarily reflect the official views of the OECD.
The data that support the findings of this study are available from Gallup but restrictions apply to their availability as access to these data was purchased for the current study. The Gallup data can be obtained for a fee after subscription to Gallup Analytics services. The raw data from Google Trends is freely available for download after subscription to Gallup Analytics services, however the terms of use prohibit distribution of these data without permission. We have complied with both Gallup and Google Trend API terms of use and services. The code used to construct the datasets and carry out the analysis is public and can be downloaded from the Open Science Framework (https://osf. io/).
Coring Early Cretaceous volcanic rocks and underlying Jurassic(?) sediments in different parts of the Mentelle Basin provides information on the timing of different stages of the Gondwana breakup and the nature of the various phases of volcanism that will lead to an improved understanding of the evolution of the Naturaliste Plateau and Mentelle Basin. Previous work by both academia and industry provided site data, including seismic data acquired in  1986, 2004, and 2009  by Geoscience Australia and limited physical material (e. g. , Deep Sea Drilling Project [DSDP] Site 258 and dredge samples adjacent to Site U1512 and on the margins of the Naturaliste Plateau), that were used to guide the initial operations plan for each site. GPS coordinates from precruise site surveys were used to position the vessel at all Expedition 369 sites.
A SyQuest Bathy 2010 CHIRP subbottom profiler was used to monitor the seafloor depth on the approach to each site to reconfirm the depth profiles from precruise surveys. Once the vessel was positioned at the site coordinates, the thrusters were lowered and an acoustic positioning beacon was dropped to the seafloor. The dynamic positioning control of the vessel used navigational input from the GPS and triangulation to the seafloor beacon, weighted by the estimated positional accuracy. The final hole position was the mean position calculated from the GPS data collected over a significant portion of the time the hole was occupied. IODP Proceedings 2 V o l u m e 3 6 9 Coring and drilling operations All four standard coring systems, the advanced piston corer (APC), half-length APC (HLAPC), extended core barrel (XCB), and rotary core barrel (RCB), were used during Expedition 369 (see Operations in the individual site chapters).
The archive halves were described by expedition scientists visually and by smear slide analyses. Finally, most of the sections were measured with the cryogenic magnetometer (see Paleomagnetism). All instrument data collected during Expedition 369 were uploaded to the IODP Laboratory Information Management System (LIMS) database (http://web. iodp. tamu. edu/LORE), and core descriptions were entered using the DESClogik application, a program used to input visual (macroscopic and/or microscopic) core descriptions (see the DESClogik user guide at http://iodp. tamu. edu/labs/documentation). When all shipboard measurements were completed, data were uploaded, and samples were taken, the cores were wrapped in clear plastic film, sealed in plastic D-tubes, and transferred to cold storage on the ship.
Lithostratigraphy The lithology of material recovered during Expedition 369 was primarily determined using macroscopic core descriptions and smear slide and thin section observations. Where necessary, digital core imaging, color reflectance spectrophotometry, X-ray diffraction (XRD), handheld portable X-ray fluorescence (pXRF), and magnetic susceptibility analyses were used to provide complementary description criteria. The methods employed during this expedition were modified from those used during IODP Expeditions 349  (Li et al. , 2015)  and 355  (Pandey et al. , 2016). The DESClogik application was used to record and upload descriptive data into the LIMS database. Spreadsheet templates were set up in DESClogik and customized for Expedition 369 before the first core was recovered. These templates were used to generate VCDs and incorporated microscopic data from smear slides and thin sections.
These additional data sets were used to quantify the texture and relative abundance of biogenic and nonbiogenic components. The sampling positions of each smear slide and thin section were recorded in Sample Master. Descriptive data were uploaded to the LIMS database and were used to produce the VCDs. The standard method of splitting cores into working and archive halves, using a piano wire or a saw, can affect the appearance of the split-core surface and obscure fine details of the lithology and sedimentary structure. When necessary, core archive halves were scraped with a stainless steel or glass slide parallel to bedding for improved results during digital imaging and to prevent cross-strati-graphic contamination during sedimentological analysis. Cleaned section halves were first scanned using the SHIL and SHMSL (see Petrophysics).
Visual core descriptions VCDs were generated after descriptions were uploaded to the LIMS database with the DESClogik software. VCDs are simplified graphical representations of each core in the case of sediments/sedimentary rocks (Figure  F3 ) and of each section for igneous and basement rocks (Figure  F4 ). Depending on the type of material recovered, one of two VCD outputs was produced in a given core: one to describe sediments or sedimentary rocks and the other to describe igneous rocks. Where both rock types were encountered in a given section, two VCDs were produced to optimize the descriptive output. A summarized lithologic description is provided at the top of each VCD. Depth and length of core sections are indicated along the left margin in the CSF-A depth scale. The graphic lithology is displayed with physical descriptions of the core that include grain size, Munsell color, sedimentary structures and lithologic accessories, bioturbation intensity, and drilling disturbance (type and intensity).
Section-half image The flat faces of the archive halves were scanned on the SHIL after splitting and scraping to avoid color changes caused by sediment oxidation and drying. The SHIL uses three pairs of advanced illumination high-current-focused LED line lights to illuminate the features of the core. A line-scan camera made by JAI imaged 20 lines/mm to create a high-resolution TIFF file, high-and low-resolution JPEG files were subsequently created. The full-size images are 508 ppi (dpi). The camera height was adjusted so that the size of each pixel was 50 μm. However, actual core width per pixel varied because of differences in surface height of each section half. All section-half image files include a grayscale and ruler. Section-half depths were automatically included with images and stored in the LIMS database.
Diffractograms of bulk samples were processed (e. g. , baseline removal and maximum peak intensity) with the software package EVA, which allowed peak characterization and mineral identification. Processed files include d-spacing values, diffraction angles, and peak intensities with background removed. D-spacing results were compared to expected values of d-spacing for minerals in the EVA database using aluminum oxide as a standard to monitor data quality. Muscovite/illite/glauconite and kaolinite/chlorite have similar diffraction patterns and could not be distinguished with shipboard results. Digital files with the diffraction patterns are available from the LIMS database.
(  2012 ) timescale with the modifications discussed below. Identification of a sequence of biohorizons in stratigraphic order allowed the recognition of biostratigraphic zones and subzones using standard schemes. These zones have been assigned absolute ages based on calibrations from other areas, mainly referenced to the paleomagnetic reversal sequence. All paleontological data gathered during shipboard investigations are available from the LIMS database in accordance with IODP policy. Species distribution data are included for every sample studied for each hole and each taxonomic group (calcareous nannofossils, planktonic foraminifers, and benthic foraminifers). Calcareous nannofossil and planktonic foraminifer data are provisional and focus mainly on biostratigraphic marker species, whereas common species of little stratigraphic value are generally omitted.
Calcareous nannofossils Calcareous nannofossil assemblages were examined and described from smear slides made from core catcher samples (at ~10 m intervals). Additional toothpick samples were taken between core catcher samples from split core sections when necessary to refine the stratigraphic position of bioevents. Slides were fixed with Norland optical adhesive and cured under UV light for examination using a Zeiss Axioscope microscope at 1000× magnification. All photomicrographs were taken using a SPOT RTS system with the   F12, F13 ). For the Mesozoic, the zonal scheme of Perch-Nielsen (1985; CC code) was used (Fig-  ures F14, F15 ). The following qualitative abundance codes were used in the DESClogik data entry program and uploaded to the LIMS database. Total calcareous nannofossil abundance in the sediment was recorded as follows: • A = abundant (>50% of sediment particles).
Archive-half sections Measurements of archive-half sections were conducted using the Integrated Measurement System (version IMS 9. 2) software with a nominal sample area parameter of 17. 5 cm 2. The measurement interval and speed were 5 cm and 5 cm/s, respectively. The response functions of the pick-up coils of the SQUID sensors have a full width of 7. 2-9. 1 cm at half height  (Acton et al. , 2017). Therefore, data collected within ~4. 5 cm of piece boundaries (or voids) are significantly affected by edge effects. Consequently, all data points within 5 cm of piece boundaries (as documented in the curatorial record) were filtered out prior to further processing. Edge ef-fects may also occur in a contiguous core piece if substantial heterogeneity (in intensity or direction) is present in the piece.
For discrete samples, we performed successive AF demagnetization with the DTech AF demagnetizer  (Model D-2000)  for spinner measurements up to 80 mT (majority of the samples) or the in-line, automated AF demagnetizer equipped with the SRM up to 80 mT. We analyzed the stepwise demagnetization data of the discrete samples by principal component analysis  (Kirschvink, 1980)  to define the ChRM. Demagnetization data analysis was performed using the PuffinPlot  (Lurcock and Wilson, 2012)  and Zplotit (http://paleomag. ucdavis. edu/software-Zplotit. html) software packages. Both the section-half data collected on the SRM and the discrete sample data acquired on the spinner magnetometer and the SRM were uploaded to the LIMS database. Low-field magnetic susceptibility of both whole rounds (see Petrophysics) and split sections (see Lithostratigraphy) was routinely measured as a proxy for the concentration of magnetic minerals.
The AGV-1, BCR-2, BHVO-2, BIR-1, JA-3, JGB-1, JP-1, and JR-2 reference materials were used for establishing calibration curves. Elemental concentrations for reference materials are given in  Imai et al. (1995)  and  Jochum et al. (2015). Agilent software was used for establishing calibration curves for the elements mentioned above. Sample results are reported as weight percent for major element oxides and parts per million (mg/kg) for trace elements. Additional reference materials were not analyzed. The wavelengths used for ICP-AES analyses are reported in Table  T11.