dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work is partially funded by grants from the  Research Council of Norway (RCN)  through its  Center of Excellence (CoE) funding scheme [RCN  Project no. 223268 ],  NIVAs Computational Toxicology Program, NCTP  (www. niva. no/nctp,RCN Project no. 160016 ) and Euratom research and training programme  2019-2020  under grant agreement No  900009  (RadoNorm).
Moreover, all of these countries face challenges from population ageing, yet it is not clear to which extent findings from one country (or set of countries) can be generalised to other institutional contexts. Thus, evidence from a wide range of countries and welfare regimes is needed. We use data covering the period 2009-2017 from four longitudinal ageing studies -the English Longitudinal Study of Ageing (ELSA), The Irish Longitudinal Study on Ageing (TILDA), the Survey of Health, Ageing and Retirement in Europe (SHARE), and the U. S. Health and Retirement Study (HRS). We use age thresholds for state pensions and Social Security benefits as instrumental variables to address selection into retirement. The results of our analysis indicate remarkable consistency in the relationship between retirement and voluntary work in all countries: retirement leads to a substantial increase in the probability of volunteering.
Overview and sample selection We use data from four longitudinal ageing studies -ELSA, TILDA, SHARE, and HRS. All four studies survey individuals aged 50 and above living in private households as well as their cohabiting partners, irrespective of age. Survey participants are interviewed every two years. Data collection in HRS started in 1992. In  ELSA, SHARE and TILDA, data collection started in 2002 , 2004 and 2009, respectively. ELSA, SHARE and TILDA were designed to ensure comparability with the HRS. The four studies have similar structures and include questions that cover comparable domains across the economic, social and health spheres. This cross-survey comparability supports the use of harmonized measures and estimation procedures in producing comparable estimates  ( Adam et al. , 2007 ; Blundell et al.
, 2021 ; Fonseca et al. , 2017 ). For a detailed description of these studies see  Sonnega et al. (2014)  ,  Taylor et al. (2007)  ,  Börsch-Supan et al. (2013)  and  Kearney et al. (2011). To facilitate comparisons across studies, we restrict the analysis to the period 2009 to 2017, in which data are available for all four studies. In particular, we use ELSA waves  5-8 (2010-2017) , TILDA waves  1-4 (2009 1-4 ( -2016 1-4 ( ), SHARE waves 4-7 (2011 1-4 ( -2017) )  and HRS waves  10-13 (2010-2016). For the analysis of SHARE, we only include the ten countries that collected information on volunteering in all four waves: Austria, Belgium, Czech Republic, Denmark, France, Germany, Italy, Spain, Sweden, and Switzerland. 3 Sample sizes for individual countries within SHARE are considerably smaller than for ELSA, TILDA, or HRS.
We therefore aggregate countries into three groups: SHARE-Nordic (Denmark and Sweden), SHARE-Central (Austria, Belgium, Czech Republic, France, Germany and Switzerland), and SHARE-Southern (Italy and Spain). Countries within these groups have similar participation rates in formal volunteering ( Eurostat, 2017 ) as well as similar welfare regimes  ( Eikemo and Bambra, 2008 ; Ferrera, 1996 ). In all datasets, we include observations for individuals aged 50-79, i. e. , around retirement age. Our final analytical samples consist of six unbalanced panels of: 7,915 individuals (22,835 observations) for ELSA; 5,965 individuals (15,738 observations) for TILDA; 20,260 individuals (55,503 observations) for HRS; 8,388 individuals (18,091 observations) for SHARE-Nordic; 29,930 individuals (64,141 observations) for SHARE-Central; and 9,523 individuals (18,176 observations) for SHARE-Southern.
We present visual evidence of the discontinuities in retirement as well as voluntary work provision in Section 5. 1. We estimate our models in an IV framework using two-stage least squares (2SLS), because certain features of our data (e. g. , age thresholds that vary across birth cohorts in ELSA, or multiple age thresholds for early and ordinary retirement in HRS and SHARE) can be more readily incorporated in 2SLS estimation than with nonparametric RDD estimators. Moreover, as noted by  Lee and Lemieux (2010)  , the identifying assumptions of IV models apply to fuzzy regression discontinuity designs as well, and we therefore can draw on several tests and falsification exercises developed for IV models to test these assumptions (see Section 6 ). We estimate the causal effect of retirement on voluntary work using the following model: First stage ∶ 𝑟 it = 1 { Ag 𝑒 it ≥ SPA } 𝜋 + Ag 𝑒 it 𝛾 1 + Ag 𝑒 2 it 𝛾 2 + Wav 𝑒 it + 𝜈 it (3) Seco nd stage ∶ 𝑦 it = r it 𝜏 + Ag 𝑒 it 𝛽 1 + Ag 𝑒 2 it 𝛽 2 + Wav 𝑒 it + 𝜀 it ∪ (4) Intuitively, in the first stage of the model we regress retirement status of individual i in year t on a binary indicator for observations above the relevant state pension age (for the HRS and SHARE data, we use two binary indicators for ERA and ORA), 1 {    ≥   } , as well as a quadratic age trend and wave dummies to obtain a predicted value for retirement status, which is independent of any unobserved confounders (these are absorbed in the error term of the model,   ).
First stage and reduced form results Table  2  shows the estimates from the first-stage of our 2SLS regression model, by study and sex. In all datasets, the instruments are significant predictors of retirement status. The probability of retirement changes by between 11 (ordinary retirement of U. S. men) and 50 percentage points (ordinary retirement for women in SHARE-Nordic). Only the threshold for early retirement in SHARE-Nordic is not statistically significant, likely because this is only identified by Sweden. The Wald F-statistic on the strength of the excluded instruments is above 100 for all models, which suggests that our results should not be affected by weak instrument problems -in other words, the relevance assumption should hold. The IV estimator can be obtained by scaling up the reduced form effect (i.
Moreover, detailed information on the type of voluntary work provided might explain some of the observed differences between countries. Unfortunately, this type of information is only available for ELSA, and we could thus not draw on it for our harmonized analysis. Another limitation of our analysis is that the measures of voluntary work provision are self-reported in the datasets we are using and might be subject to recall bias. Moreover, the frequency of volunteering is only reported in broad categories. Unfortunately, comparable prospective data (e. g. , from time use diaries) are to our knowledge not available. Although our measures of volunteering are broadly comparable, there are some differences in the questions used across included surveys, which might affect our results. More generally, while our harmonised analysis provides important insights for a range of institutional settings, it also limits the depth of our analysis.
Data availability ELSA data is available free-of-charge from the UK Data Service (https://ukdataservice. ac. uk/). TILDA data can be downloaded free-of-charge from the Irish Social Science Data Archive (www. ucd. ie/issda/), Gateway to Global ageing (www. g2aging. org/) and Interuniversity Consortium for Political and Social Research (www. icpsr. umich. edu/icpsrweb). Researchers will also need to apply to the TILDA Statistics and Data Management Team, based at Trinity College Dublin (email: tilda@tcd. ie), for access to variables not included in these publicly-available files. HRS data is publicly available at the study website ( https://hrs. isr. umich. edu/data-products ). This paper uses data from SHARE Waves 4, 5, 6, and 7 (DOIs: 10. 6103/SHARE. w5. 710, 10. 6103/SHARE. w6. 710, 10. 6103/SHARE.
Acknowledgments We would like to thank  Bruno Arpino ,  Eric Bonsang ,  Jo Mhairi Hale ,  Donal O'Neill ,  Miquel Pellicer ,  Olive Sweetman  and  Robert Wright  for their helpful comments and suggestions. We would also like to thank seminar participants at the  University of Torino, University of Southampton , and participants of the "Ageing of the Baby Boomer " workshop at the  Federal Institute for Population Research  for their helpful comments and suggestions.
Purpose: The purposes of this study were to investigate a radiographic sign found on coronal magnetic resonance imaging (MRI) sequences in subscapularis (SSC) pathology, reporting interobserver reliability data and sensitivity and specificity; and to correlate the preoperative assessment of SSC pathology with intraoperative assessment of the SSc during shoulder arthroscopy. Methods: A consecutive series of patients undergoing arthroscopic rotator cuff repair from January 2020 to December 2020 were examined. The positive diagnosis of a subscapularis tendon tear was prospectively determined and confirmed by arthroscopy. The "digitation sign" was assessed by 3 independent shoulder-trained fellows when evaluating the SSC in the coronal plane on T2-weighted images. Results: Of the 132 patients included, 74 (56%) had SSC tendon tears confirmed during arthroscopy.
Statistical Analysis Statistical analysis was conducted comparing SSC tendon tears recognized during shoulder arthroscopy (gold standard) to those diagnosed preoperatively by MRI. In addition, descriptive data analysis was conducted depending on the nature of the considered criteria. For quantitative data, this included number of observed values, mean, standard deviation, median, and minimum and maximum. For qualitative data, this included the number of observed values and the number and percentage of patients per class. Comparison between variables was assessed using chi-squared or Fisher's exact test for categorical variables and Student's t test or Wilcoxon test for quantitative variables.
A. Configurations 1) Test Data: Multichannel reverberant speech signals used for evaluation were synthesized by convolving single-channel dry speech signals with room impulse responses (RIRs) taken from the development and evaluation subsets of the REVERB Challenge dataset  [50]. The RIRs were measured with an eightchannel circular array with a diameter of 0. 2 [m] under six conditions where the reverberation time RT 60 was 250, 500, or 700 [ms] and the distance between the source and array was 0. 5 (near) or 2. 0 [m] (far). To compensate for the time gaps between the source and microphones, multichannel dry speech signals were also synthesized as ground-truth data by using non-echoic RIRs obtained by masking the original RIRs except at the peak and its previous and next two samples. Through all experiments, audio signals were sampled at 16 kHz and processed by STFT with a shifting interval of 256 samples and a Hann window of 1024 samples (F = 513).
2) Training Data: The DNN-based source model was obtained as the decoder of a VAE trained from clean dry speech signals taken from the training subset of the REVERB Challenge dataset  (14. 8 [h]  in total). The configuration of the VAE was the same as that of  [19]  except for the dimensions of the observed and latent spaces (F = 513 and D = 16 in our work and F = 2048 and D = 256 in  [19] ) and the speaker conditioning  [19]  was not used. There was no overlap between the speakers of the training data and those of the test data. The volume of each utterance was perturbed with a uniformly distributed random number between 0. 5 and 1. 5. 3) Optimization: The number of iterations was set to 150 for achieving convergence (100 and 150 iterations for M = 3 and M = 8 were sufficient in our preliminary evaluation, respectively).
B. Comparison of Source and Reverberation Models Using reverberant speech signals, we investigated the combinations of the NMF-based, DNN-based, FI, and non-structured source models and the AR, MA, and ARMA reverberation models in a speech dereverberation task. 1) Experimental Conditions: Twenty reverberant speech signals were randomly selected from the development subset of the REVERB Challenge dataset  [50]  under each of the six conditions (120 signals in total). Each signal included only a single utterance without noise contamination. The hyperparameters were set as N = 1 (N speech = 1 and N noise = 0), M = 8, L MA = 8, L AR = 4, and ∆ = 3. We compared ARMA-FastMNMF(-DP) with K ∈ {4, 16, 64} with its ablated versions, AR-and MA-FastMNMF(-DP), where FastMNMF-DP used only the DNN-based source model for a single speech source.
C. Investigation of Hyperparameters and Optimizers Using reverberant speech mixtures, we investigate the performance of ARMA-FastMNMF according to the hyperparameters L MA , L AR , and ∆, and the optimizer (IP, ISS1, or ISS2) in a speech separation and dereverberation task. 1) Experimental Conditions: Twenty reverberant speech mixtures were made from the REVERB Challenge dataset  [50]  under each of the six conditions (120 signals in total). Each mixture was obtained by superimposing a reverberant speech signal randomly taken from the development subset and another one from the evaluation subset. We tested ARMA-FastMNMF with N = 2, M ∈ {3, 8}, and K = 16. We investigated the impact of the delay parameter ∆ ∈ {2, 3, 4} using L MA = 0 and L AR ∈ {2, 4, 8, 16}. We then investigated the combination of L MA ∈ {0, 2, 4, 8, 16} and L AR ∈ {0, 2, 4, 8, 16} using ∆ = 2 for M = 3 or ∆ = 3 for M = 8.
D. Comparison in Noisy Environments Using noisy reverberant speech mixtures, we compared ARMA-FastMNMF(-DP) with the state-of-the-art methods in a speech separation and dereverberation task. 1) Experimental Conditions: 100 noisy reverberant speech mixtures were made from the REVERB Challenge dataset  [50]  under each of the six conditions (600 signals in total). Each mixture was obtained by superimposing a reverberant speech signal randomly taken from the development subset, another one from the evaluation subset, and a real diffuse noise signal (mainly caused by air conditioners) from the development or evaluation subset. The SNR of the clean speech mixture was set to 0 dB. We tested ARMA-FastMNMF(-DP/TI) and ARMA-FastFIA with M ∈ {3, 8}, L MA ∈ {0, 8}, L AR ∈ {0, 4}, The delay parameter was set as ∆ = 2 for M = 3 and ∆ = 3 for M = 8 and the number of bases was set as K ∈ {4, 16, 64} for ARMA-FastMNMF and K = 16 for ARMA-FastMNMF-DP/TI.
The Valgarður database is a compilation of data describing the physical and geochemical properties of Icelandic rocks. The dataset comprises 1072 samples obtained from fossil and active geothermal systems, as well as relatively fresh volcanic rocks erupted in sub-aerial or sub-aqueous environments. The database includes petrophysical properties (effective and total porosity, grain density, permeability, electrical resistivity, acoustic velocities), as well as mineralogical and geochemical data obtained by point-counting, X-ray Fluorescence (XRF), quantitative X-ray Diffraction (XRD), and Cation Exchange Capacity (CEC) analyses. The motivation behind this database is threefold: (i) aid in the interpretation of geophysical data including uncertainty estimations, (ii) facilitate the parameterization of numerical reservoir models, and (iii) improve our understanding of the relationship between rock type, hydrothermal alteration and petrophysical properties.
Structure and Contents of the Database Valgarður is a publicly accessible database containing petrophysical and chemical/mineralogical analyses of Icelandic rocks. Although many studies have investigated the effect of elevated temperature and pressure on the petrophysical properties of Icelandic rocks (e. g. Vinciguerra et al. , 2005; Jaya et al. , 2010; Kristínsdóttir et al. , 2010; Milsch et al. , 2010; Adelinet et al. , 2010; Adelinet et al. , 2013; Grab et al. , 2015; Eggertsson et al. , 2020a,b; Nono et al. , 2020; Kummerow et al. , 2020; Weaver et al. , 2020) , at present we restrict the database to measurements at near-ambient conditions, in order to facilitate comparison between the different studies and ensure consistency among the reported data. Sample collection involves drilling a ~2.
5 cm diameter plug of variable length into a surface outcrop or section of core (Figure  1 ). Variability in the sample collection process and analytical methods is to be expected given the long period over which the underlying data comprising the database were collected. During assembly of the database, we sought to ensure that the results of the different studies are reported in a consistent manner. As different studies used different methods to analyze given petrophysical properties, each data point is accompanied by a description of the methodology or origin of the data. Table  1  shows a description of the sources of the data for the database. The original 529 samples collected by Orkustofnun and Iceland Geosurvey (ÍSOR) between 1990-2010 that made up the first release of the Valgardur database mainly originate from hand-drilled cores taken at the surface within the neovolcanic zone or at erosional surfaces in the older strata at various.
Quantitative mineral characterization using XRD. palaeo-depths and alteration stages  (Sigurðsson and Stefánsson, 1994; Guðmundsson et al. , 1995; Franzson et al. , 2007; Friðleifsson & Vilmundardóttir, 1998; Franzson et al. , 2011). In this release of the database, we added data from 302 samples collected by  Orkustofnun between 1970 -1980 (Pálsson, 1972; Pálsson et al. , 1984) , 161 samples from downhole cores obtained from active geothermal systems  (Flovenz et al. , 2005; Franzson & Tulinius, 1999; Bär et al. , 2019; Lévy et al. , 2018 Lévy et al. , , 2019a Lévy et al. , , 2019b Lévy et al. , , 2020a Lévy et al. , , 2020b;; Gilbert et al. , 2020; Nono et al. , 2020) , as well as previously unpublished analyses from borehole samples from the Theistareykir geothermal area and surface samples from the Austurhorn gabbro.
Although many studies have investigated the geochemistry and petrology of Icelandic rocks (e. g. Sigmarsson and Steinthórsson, 2007; Sigmarsson et al. , 2008)  and much of the available data has been compiled into a publicly-accessible database  (Harðardóttir et al. , 2022) , in order to restrict A third table lists the names of files which contain images of many of the sampling locations for studies. These photographs have been included with the database to facilitate future investigations in the area. The fourth worksheet lists additional measurements performed of a subset of hyaloclastite tuff samples  (Frolova et al. , 2005) , which are not included in the main database due to different methodology, i. e. , effective porosity measurement using air as the saturating fluid rather than He gas (see below).
Sample ID and References The order in which samples are presented in the database is approximately chronological, based on the date of the reference and the order of the sample numbers within the reference. The sample ID is equivalent to the reported sample ID in the primary reference. Primary references indicate where the data was first published and/or best-documented; secondary references are also given if the data was reported or used in further studies, or in the case of borehole samples, provide the core logs reported after the drilling of the well. References that are not cited in this text but are cited in one of these columns are described in the references worksheet provided in the Excel database.
For many of the samples, photographs were taken which show in some detail the location and the geological features (Figure  1 ). These photographs are provided in a supplement to the database. The latitude and longitude of the sampling point at the surface in decimal degrees is reported according to the reference system WGS84 as well as in ISNET93, the latter being widely used in Iceland. The elevation is also shown in meters above sea level (m a. s. l. ) and was obtained using Google Elevation services when not provided in the primary reference. Borehole samples show the coordinates at the wellhead and an accompanying depth (in meters), which may be uncertain for some samples collected by  Pálsson et al. (1972 Pálsson et al. ( , 1984)). Figure  2  shows the locations of all obtained surface and borehole samples.
Silicic intrusions include both rhyolite dykes as well as granophyre, which usually shows fine-scale intergrowth of quartz and feldspar, as well as microgranite, which lacks granophyric intergrowths. Intermediate intrusive rocks including diorite are relatively rare. It should also be envisaged that intrusions may be of very variable age in relation to the geothermal system it intrudes. Sedimentary rocks include clay-rich lacustrine sediments, glacial tillite, conglomerates, sandstone, as well as interbasaltic beds. Further distinction between these rock types is not made in the database due to the emphasis on volcanic and igneous rocks. It should though be noted that sedimentary grains are near exclusively of igneous origin. In addition to the lithological classification, each sample is assigned to one of the main alteration zones identified in Icelandic rocks (Table  3 ).
, 2011)  or air  (Frolova et al. , 2005)  as the saturating gas, and shows that effective porosity of samples measured using air is lower, likely due to the lesser ability of air to penetrate the microporosity. However, as these measurements were performed on different core plugs obtained from a given rock outcrop (e. g. Fig. 1d ), this uncertainty may also reflect the natural heterogeneity in the sampled rock. All of the total porosity and most of the grain density data reported in this database were determined by pulverizing the sample and measuring the density of crushed materials using conventional techniques (e. g. Hg displacement). Total porosity can be calculated following Eq. (  3 ): 𝜙𝜙 𝑠𝑠𝑝𝑝𝑠𝑠 = 𝑉𝑉 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 -𝑉𝑉 𝑔𝑔𝑝𝑝𝑚𝑚𝑚𝑚𝑔𝑔 𝑉𝑉 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝜌𝜌 𝑔𝑔𝑝𝑝𝑚𝑚𝑚𝑚𝑔𝑔 -𝜌𝜌 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝜌𝜌 𝑔𝑔𝑝𝑝𝑚𝑚𝑚𝑚𝑔𝑔 (3) As noted in  Colombier et al.
The Klinkenberg coefficient and intrinsic permeability   are obtained by plotting measured gas permeability at a range of pressures against 1   , with the slope corresponding to   and the y-intercept   (when   goes to infinity). The Klinkenberg coefficient depends on various properties of the rock, particularly the geometry of the pore space, and generally increases with decreasing permeability. As a result, relative measurement accuracy is greater (±5%) for high permeability rocks (≥10 -14 m 2 ) and reaches up to ±400% in low permeability rocks (≤10 -16 m 2 )  (Filomena et al. , 2014). For samples from  Lévy et al. (2019 Lévy et al. ( , 2020)) , only Klinkenberg-corrected permeability is available. Measured permeabilities listed in the database range over six orders of magnitude, from ~10 -18 m 2 (near the lower limit of many measurement techniques) to ~10 -12 m 2.
Although the four-electrode set-up improves the quality of the conductivity spectra, especially below 10 kHz, the values obtained by both set-ups are comparable at 1 kHz, where effects of electrode polarization are negligible  (Lévy et al. , 2019b). In the Valgarður database, resistivity measurements (inverse of in-phase conductivity) at low-salinity and room temperature are presented; in addition, the frequency and experimental configuration (two-or four-electrode) are noted in the "Remarks" column next to each data point. In rocks where free ions in pore water are the only charge carriers, the in-phase conductivity of a volume of rock   is governed by Archie's law: 𝜎𝜎 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 = 𝜎𝜎 𝑤𝑤 𝐹𝐹 (9) where   is the conductivity of the pore fluid and  is the formation factor, representing the tortuosity of the current path.