dataesr/scientific-paragraphs-categorization huggingface dataset:
This was repeated until no further parameter significantly improved the model. Applying this procedure allows us to deal with the collinearity of the predictor variables (see To exclude the impact of overly influential outliers in the final models, data points were removed on the basis of a threshold of 2. 5 SD standardized residual errors after having defined the models, and the models were then re-fitted on the truncated dataset (model criticism;  Baayen, 2008). All frequency estimates, response times in the lexical decision tasks, as well as first-fixation durations and gaze durations in the eye-tracking study were log-transformed  (Baayen & Milin, 2010). All analyses were conducted using the packages lme4  (Bates, Mächler, Bolker, & Walker, 2015)  and lmerTest  (Kuznetsova, Brockhoff, & Christensen, 2017)  for R (R Core Team, 2017).
Thus, the target words in the material were the original 2,154 real compound words (e. g. Flughafen, airport), while the 2,154 nonwords consisted of one real-word constituent and one nonword constituent (e. g. Flughafan, airpurt). Nonwords were created this way in order to ensure the highest possible form similarity to the target words, and therefore to minimize confounding (for example, the impact of orthographic cues;  Seidenberg & McClelland, 1989). Experimental data were collected from 16 native German speakers. The material was split into two lists, each containing half of the actual-word stimuli from the word material, and the other half in their nonword versions. Each participant was presented with one of these lists, divided into eight experimental blocks. Participants were instructed to indicate whether the letter strings presented to them where actual, existing German words.
Except for the nonwords, this second lexical decision experiment was identical to the first one. Data were collected from 16 native German speaking participants. Again, each participant was presented with half of the actual-word stimuli and half of the nonword stimuli from the word material (for more details, see  Böhl, 2007). Target Words. Error trials and trials with response times larger than 1600 ms were again removed before the data were aggregated over participants. The parameter for modifier frequency did not significantly predict response times and was thus removed from the baseline model. In the first screening step, only the inclusion of modifier composition significantly improved the baseline model (X 2 (1) = 5. 85, p =. 016, b = -0. 08, t = -2. 44). Accordingly, the final model after forward selection (see Table  6 ) only contains modifier composition as a semantic parameter.
The measures are provided at https://doi. org/10. 6084/m9. figshare. 8295101, along with the other datasets. Error trials (that is, trials in which participants erroneously responded that the compound presented to them is an existing word) and trials with response times larger than 1600 ms were again removed before the data were aggregated over participants. Since the compound frequency for all the compounds is zero, the parameter was not included in the baseline model. Furthermore, modifier and head frequency both did not predict reaction times and were not included in the baseline model. In the first screening step, there only was a borderline effect of modifier composition (X 2 (1) = 3. 80, p =. 051, b = 0. 04, t = 1. 96), but no effect of head composition or constituent similarity. However, after the removal of overly influential data points via model criticism, the final model contained a significant effect of modifier composition (see Table  6 ).
Eye-Tracking with the Boundary Technique The final validation experiment contains data collected in an eye-tracking study with the boundary technique  (Beauvillain, 1996; Rayner, 1975). In this experimental paradigm, participants have to read a word presented on a screen and understand its meaning. To ensure that the participants actually access the word meanings, participants then have to read a second word (which could be a noun, verb or adjective) and decide whether the two words are semantically related. The data for this experiment were collected from eight participants, who each were presented with the full set of word material. The items were presented to the participants in four experimental sessions à four blocks each. In each trial of this experiment, a fixation cross was first presented at the left margin of the screen (100 pixels to the right of the left margin).
After participants fixated the cross, both words were presented simultaneously, with the target word on the left and the second word on the right hand side of the screen. The second word was masked with hashs, which were removed once the target word has been read and participants made a saccade across an invisible boundary between the two words. At that point, the target word was masked and so remained until the end of the trial. Participants then had to decide whether the two words were related. For more details on the experimental procedure, see  Böhl (2007). First fixation durations and gaze durations (defined as the sum of all fixations on the target word) were collected. First fixation durations lower than 70 ms were removed from the data, as were gaze durations lower than 200 ms or higher than 1600 ms and trials with incorrect answers in the semantic relatedness judgments (following  Böhl, 2007) , before the data were aggregated over participants.
Practical Applications The practical uses for the datasets provided in the present study are manifold: On the one hand, the included semantic transparency measures can be used to investigate, or control for, semantic effects in new psycholinguistic studies, or to thoroughly examine such effects in existing studies and datasets. Furthermore, they can be used as quantitative reference points in order to empirically test the predictions of linguistic theories on the semantics of compounding. On the other hand, the included behavioral measures -ratings, lexical decision response times, and eye-tracking measures -can serve as a gold standard for the evaluation of new models and theories on compounding, with the semantic measures provided here serving as a reference benchmark. In combination with the English resources already available in the literature (especially with the set by  Günther & Marelli, 2018a , providing comparable computational measures), the present dataset can also be applied to investigate cross-linguistic issues.
Ambient air measurements were alternated with measurements of air from a reference cylinder. The cylinder contained air with 1974. 0 ppb CH 4 , and isotope values of d 13 C ¼ À47. 75 ± 0. 05& vs V-PDB and dD ¼ À87. 9 ± 1. 1& vs V-SMOW, linked to a previous calibration against the international standard material at the Max Planck Institute for Biogeochemistry, Jena, Germany  (Sperlich et al. , 2016). After processing, the data resulted in time series for d 13 C-CH 4 and dD-CH 4 in ambient air, at a non-regular interval of 51 minutes on average. CH 4 mole fractions were measured continuously by two CRDS (cavity ring-down spectrometry) instruments (model G2301 until 13/12/2016 followed by model G2401 since then, Picarro inc. , CA, USA) connected to the same inlet as the IRMS. A set of instrument-specific empirical water vapor correction factors were used to derive CH 4 dry air mole fractions  (Chen et al.
Meteorological data. Hourly measurements of wind speed and wind direction were used to interpret the methane time series. The meteorological data collected at Lutjewad at the different heights (7, 40 and 60 m above ground) were incomplete with missing data from 23 January 2017, onwards. Therefore, another dataset was used, from a nearby station of Lauwersoog (53 25'N 6 12'E), operated by The Royal Netherlands Meteorological Institute (KNMI). It is situated at about 10 km from the Lutjewad tall tower, and wind measurements at 10 m height are available for the entire measurement period. Both datasets show very similar wind characteristics despite the spatial and elevation difference.
0 W-12. 0 E] in longitude. For the simulations, 29 vertical levels were used, reaching up to a top pressure of approximately 300 hPa. The meteorological data used to drive the model were taken from the European Centre for Medium-Range Weather Forecast (ECMWF) operational forecast product with a 10 km horizontal and 3-hour temporal resolution. The boundary and initial v(CH 4 ) conditions were obtained from the analysis and forecasting system developed in the Monitoring Atmospheric Composition and Climate (MACC) project  (Mar ecal et al. , 2015). The version used for this study consists of 71 vertical levels, a horizontal resolution of 0. 563 Â 0. 653 , and a temporal resolution of three hours. The meteorological products and the mole fraction fields were interpolated to the model domain both spatially and temporally by the PYVAR-CHIMERE system.
The Keeling plot method was applied in a similar way as in R€ ockmann et al. (  2016 ), but with slight modifications. The moving time window had a width of 12 h of data and moved in steps of 1 h. At each step, v(CH 4 ) values below the lower 10% percentile, and <2100 ppb, taken within a larger 24 h window were also included as background. A minimum number (n) of 5 points and a v(CH 4 ) range of at least 200 ppb were used to select suitable datasets in these time windows. An orthogonal distance regression was then applied to determine the intercept of the d m : 1=c m correlation and its uncertainty. Only linear fits with a standard error of the regression s < 2. 5& were selected for further evaluation. This represents the typical distance between the data and the regression line. If this condition was not fulfilled, the window was narrowed by 1 h, until either n < 5 (rejected) or s < 2.
5& (selected). The signatures obtained were then filtered for those with a well-defined isotopic composition, indicating that the dataset can be fit assuming a source with a constant isotope signature. The following criteria was applied: r intercept < 1:5& for d 13 C 30& for dD and r winddir: <90 8 with r intercept and r winddir: being the standard deviation of the regression intercept and of the wind directions in the window. Applying these criteria filtered out 14% of the initial signatures from the moving window Keeling plot in total. The r intercept threshold filters out more data than the one for the r winddir: : In some cases, several regression intercepts were derived from the same pollution event, i. e. when a v(CH 4 ) peak was longer than 12 h. In this case, they were averaged over the duration of the peak to obtain one source signature per pollution event.
Overview The complete dataset measured over 5 months is shown in Fig. 2. The gaps in the data are caused by technical failures. The v(CH 4 ) time series shows pronounced variability, compared with measurements from the Mace Head background station in Ireland made by the Advanced Global Atmospheric Gases Experiment (AGAGE,  Prinn et al. , 2008). In the first half of the period (until mid January 2017), CH 4 elevations were interrupted by periods when background values prevailed over longer periods. For example, the v(CH 4 ) stayed stable and matched Mace Head values over a few days in the end of December 2016. The average v(CH 4 ) measured at Mace Head was 1950 ± 39 ppb, which compares well with the observed background value of 1933 ± 11 ppb from Lutjewad (average of the lower 10% percentile of v(CH 4 )).
The d 13 C time series can also be compared to measurements from flask samples taken at Mace Head, by the National Oceanic and Atmospheric Administration (NOAA;  Dlugockenky et al. , 2019; Fig. 2). No measurements are available for dD, but we expect dD values at Mace Head to match the background values from the Lutjewad measurements in a similar way as for d 13 C. CH 4 elevations in the Lutjewad dataset are systematically accompanied by negative excursions of both d 13 C and dD values. The isotopic values reached down to À51. 8& for d 13 C and À147& for dD, whereas the average background was À47. 7 ± 0. 21 and À84. 2 ± 5. 2&, respectively. The background from the measurements, calculated as the average of the 10% lower percentile of v(CH 4 ), compares well with the average d 13 C of À47. 7 ± 0.
Source signatures From the Keeling plots of the entire observations dataset (Fig. S4(a, b )), the total averaged source signatures are À59. 55 ± 0. 13& and À287. 2 ± 1. 4& for d 13 C-and dD-CH 4 , respectively. These values are typical for microbial (including waste) methane emissions, in agreement with the source attribution from the model. The results from the moving window Keeling plots also show a prevalence of isotopically depleted sources: the mean isotope signatures of the evaluated peaks are respectively À60. 3 ± 3. 1& and À286. 3 ± 27. 5& for d 13 C and dD. These values agree within the uncertainties with the Keeling plot intercepts made with all points of the    (Hitchman, S. P. 1989). This already shows that the observed methane elevations contain only relatively small fractions of thermogenic methane.
7 ). The isotope source signatures of the first two events are between À64. 3 and À62. 6& for d 13 C and between À323 and À297& for dD in the measurements, but increase to À55. 1& for d 13 C and À233& for dD during the third event. The model captures the isotope signatures of the three events relatively well when the EDGAR inventory is used. The TNO-MACC III inventory shows an increase in fossil fuel-related emissions on December 19th and 20th, and a corresponding d 13 C enrichment. For event 2, this is not confirmed by the measured d 13 C values, which indicate a prevalent biogenic source. For event 3, the measured dD source isotopic signature reaches À232 ± 6. 7&, the highest value derived from this dataset. In Fig. 5 , this point clearly falls outside typical isotope signatures for biogenic sources and waste.
The associated d 13 C-CH 4 is À55. 1 ± 0. 71&. It does not correspond to the typical North Sea gas source signature, which is usually more enriched (Fig. 5 ). Yet it is also among the highest d 13 C values derived from this dataset. This strongly suggests that this pollution event contained a higher proportion of CH 4 from non-biogenic sources on land (because of a southern wind). Event 3 was not elevated enough in the model using TNO-MACC III to allow calculation of the isotope source signatures (selection criteria (see section 2. 4) were not fulfilled). Both emission inventories show a higher proportion of wetland emissions, combined with a relatively large fossil fuel contribution in EDGAR v4. 3. 2. This also caused higher signatures for this event from the model results, but not as much as in the measurements, likely because of the additional contribution of isotopically depleted CH 4 from wetlands.
Spatial and temporal variability There is a seasonal cycle in the background methane mole fractions in the northern hemisphere due to the higher abundance of OH radicals in the summer  (Dlugokencky et al. , 2011). The dataset presented here was taken over the fall and winter months and is not strongly affected by this annual variability. The background v(CH 4 ) measured at Mace Head were stable over the 5 months of measurements, and were in good agreement with the measured values when the wind was coming from the west. Diurnal variability is to a large degree driven by the accumulation of compounds from surface emissions during the night, and more vertical mixing during the day. During the two episodes presented in detail in the results section, the highest v(CH 4 ) elevations were recorded in the beginning of the day.
The nighttime accumulation is therefore visible in the measurements almost everyday, at least during the winter months. On March 10, no elevation was recorded: the average v(CH 4 ) was 1954 ± 13 ppb from 00:00 to 22:00, which is close to the the overall background of 1933 ± 11 ppb (section 3. 1), and the values observed at Mace Head. This  Agriculture 58. 6 ± 12. 0 62. 3 ± 12. 9 Fossil fuels 14. 5 ± 7. 9 10. 7 ± 5. 9 Waste 17. 9 ± 7. 8 19. 0 ± 9. 6 Wetlands 6. 1 ± 3. 5 5. 7 ± 2. 7 Others 3. 0 ± 2. 5 2. 3 ± 1. 0 part is highlighted in Fig. 8  (white hatching). The wind in the period March 9, 16:00 to March 10, 18:00 was from west/northwest (300 ± 14 N), bringing background air from the sea. In general, background values in the dataset were mostly advected by winds from 250 to 360 N. Variations in wind direction on longer time scales also affect the results: southerly to southwesterly winds were prominent in December and January, whereas easterly winds, from 80 to 100 N, almost never occurred.
Southwesterly winds advected the highest mole fractions of the dataset (Fig. 3(c, d )), corresponding to important biogenic CH 4 sources. They can be attributed to agriculture (mainly cattle farming), waste management, and to a smaller extent, wetlands (Fig. 7 , event 1). Easterly winds did not bring air with very high v(CH 4 ), but some of the elevations were associated with a significantly higher d 13 C (Fig. S5 ). Emissions from the Groningen gas field can potentially be the cause of this enrichment. Both EDGAR v4. 3. 2 and TNO-MACC III inventories report large fossil fuel emissions in northwest Germany, which may have also contributed. There is no clear evidence, however, of this higher fossil fuel contribution from the dD results. Fewer dD signatures were obtained from easterly winds.
Source identification The resulting source isotopic signatures clearly confirms that at Lutjewad station the dominant sources are microbial. This includes emissions from the waste sector, but these source categories are not easy to disentangle since the source isotopic signatures partially overlap (Fig. 5 ). The emission sources that characterise the area are regional human activities such as cattle farming and waste management. The isotopic analysis gives a consistent interpretation of the methane source contributions, and is confirmed by the modelling exercise. From November 2014 to March 2015, similar measurements were performed at the Cabauw tall tower site, in central Netherlands  (R€ ockmann et al. , 2016). The resulting source signatures derived from the entire datasets are compared in Table  3.
The average source signature and the ones of individual events from the CHIMERE model results are closer to the observations when using EDGAR v4. 3. 2 (Table  2 , Figs. 7  and 8 ). Table  4  shows the average source signatures resulting from the Keeling plot of the entire dataset. The source signatures are overestimated when using both models, with both inventories. The best agreement for the average source signatures is with the CHIMERE results using the EDGAR v4. 3. 2 inventory. Wetland CH 4 emissions were not taken into account in FLEXPART-COSMO, which explains the relative enrichment in d 13 C compared to the CHIMERE results. If the wetland emissions are neglected in CHIMERE, the average source signature would change by þ0. 7 and þ7& respectively for d 13 C and dD. Taking these emissions into account could significantly improve the agreement between the results from FLEXPART-COSMO and the observations.
Funding This work was supported by the  ITN  project  Methane goes Mobile -Measurements and Modelling  (MEMO 2 ; https://h2020-memo2. eu/). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  722479. AGAGE is supported principally by  NASA (USA)  grants to MIT and  SIO , and also by:  BEIS (UK)  and  NOAA (USA)  grants to Bristol University;  CSIRO  and  BoM (Australia) :  FOEN grants  to  Empa (Switzerland) ;  NILU (Norway) ;  SNU (Korea) ;  CMA (China) ;  NIES (Japan) ; and  Urbino University (Italy).
Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1. 2 and of the HC5 2. 3 compared to 1. 2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (> 10 5 ) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10 12. We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.
, 2001; Landis and Chapman, 2011) , also valid for comparative assessments  (Fantke et al. , 2018). Moreover, EC50s are typical endpoints of the available in silico methods  (Netzeva et al. , 2007) , and especially of the ICE equations our approach is based on. Finally, the limited number of other measured chronic endpoints was another motivation for the use of acute EC50 values, e. g. , 7369 acute SSDs compared to 1,051 chronic SSDs derived from measured data collated in the database of  Posthuma et al. (2019).
In addition, the VEGA QSARs also provide a global applicability domain index (ADI) per estimated ecotoxicity value which is calculated by grouping several indicators taking a particular issue of the applicability domain into account  (IRFMN, 2017a (IRFMN, , 2017b)). We considered a chemical as within the QSAR's applicability domain when the ADI was above 0. 7  (Cappelli et al. , 2015; Golbamaki et al. , 2014; IRFMN, 2017a IRFMN, , 2017b)). The lack of a consistent dataset with experimental algal test results and the variability of these results make it difficult to derive globallyapplicable QSARs for algal toxicity  (Fu et al. , 2015; Netzeva et al. , 2007; Villain et al. , 2014). The available models are either proprietary themselves or based on predictors requiring proprietary software  (Bakire et al.
, 2018; Singh et al. , 2014). We therefore chose to use two QSARs: one developed for pharmaceuticals and one for personal care products  (Gramatica et al. , 2016; Sangion and Gramatica, 2016). These QSARs estimate the ecotoxicity to P. subcapitata based on 2D chemoinformatic descriptors estimated with the PaDEL software  (Yap, 2011). These QSARs were not applicable for a large set of chemicals but fulfilled all the other criteria. We used the QSARINS software v2. 2. 2 to choose the chemicals entirely within the AD of the QSAR  (Gramatica et al. , 2014 (Gramatica et al. , , 2013)). Whenever chemicals were within the AD of both QSARs, the QSAR derived for pharmaceuticals was preferred given the larger training set it was developed with.
Statistical uncertainty The statistical uncertainty in the HC50 EC50 and HC5 EC50 is caused by uncertainty in the QSAR and ICE models as well as sampling uncertainty due to limited number of species available. We ran a Monte Carlo simulation with 10,000 iterations to propagate these three uncertainty sources into an uncertainty estimate for the HCs. This procedure comprises three consecutive steps: 1. EC50s were estimated from the available QSARs, including their associated uncertainty. This uncertainty was quantified following  Mendenhall et al. (2009)  (SI, S2), using the details of the QSAR training data sets and the reported mean squared errors  (Gramatica et al. , 2016; IRFMN, 2017a IRFMN, , 2017b;; Sangion and Gramatica, 2016). Per chemical, each Monte Carlo iteration resulted in three possible QSAR-based EC50 values (i.