dataesr/scientific-paragraphs-categorization huggingface dataset:
Data selection All submitted observations were retained in the database, and the summary presented below is based on all the data in the database. But the analysis (development of statistical models) presented here was carried out on a subset of the plot-level dataset. Data for cattle slurry applied to grassland were used for analysis, because it was the largest most-balanced subset (819 observations in total, 485-568 used in the models). Observations with less common types of manure (see Section 3. 1) were excluded. The complete database also included solid manures with high DM (up to 37% for cattle manure), but those with values above 15% were excluded from the analysis, since the focus was on slurry. Six observations did not include a DM measurement and were also excluded. Lastly, three application methods with few observations (band spreading on slots (institute 203 only), closed slot, and pressurised injection (institute 102 only)) were excluded.
Statistical models Factors affecting emission (including research institute) were explored to identify and quantify important effects, and highlight some challenges for data analysis and use of the database. Two types of models were used: linear mixed-effects models applied to complete data subsets (Section 2. 2. 1), and linear fixed-effects models applied separately to results from individual experiments. For both, log 10 -transformed 48 h cumulative emission (absolute or normalised by TAN application rate to give a relative value) was the response variable. Selection of 48 h was arbitrary. A shorter time would increase the size of the data subset, but emission values would be farther from "total" emission. For this subset, 48 h emission was generally close to final emission (median value: 94%) while 24 h emission was substantially lower (median value: 86%).
Or, a 1% increase in slurry dry matter would always have the same fractional effect on emission. While probably not completely accurate, these responses are more reasonable than those for no transformation (additive effects of predictors and constant absolute effects on emission). With the transformation, the response variable (cumulative emission) can never be below zero, but there is no upper bound on emission, which could then be greater than applied TAN. However, for this relatively large and varied dataset, only a small number of predicted values exceeded applied TAN. (Interpretation of coefficients when the response or predictor variables are log 10 -transformed is discussed in Supplemental file S6). Mixed-effects models were used to quantify effects of slurry characteristics, environment, management, and unexplained effects associated with institutes.
g. ,  Huijsmans et al. , 2001; Misselbrook et al. , 2005; Thorman et al. , 2008). Most database variables could affect NH 3 emission, but other possible predictors were missing from a large number of observations, and so were not considered. However, the variable set selected above is arbitrary, and other sets with fewer or more observations may be reasonable (and would produce different model results). Slurry TAN concentration and application rate were log 10 -transformed to allow for the possibility of a direct relationship between their values and emission rate. When necessary, wind speed was adjusted to a reference height of 2. 0 m based on Eq. (2. 55) in Guyot (1998) (Eq. (  2 )). = v v h z h z ln( / )/ln( / ) r m r m 0 0 (2) In Eq. (  2 ), v = wind speed (m s -1 ), h = height of measurement (m), z 0 = roughness parameter (m), and subscripts r and m indicate reference and measurement.
Two random-effect predictors were included in the mixed-effects models: institute, and a factor for emission measurement method/soil type combination nested within institute (along with an implied residual error term). These effects are expected to include effects of variables not measured and any biases in application and measurement methods, as discussed in more detail in Section 3. 3. It is reasonable to expect dependencies among all observations from within a single institute, but also among observations made using the same measurement technique (in cases where more than one technique was used by a single institute) and at the same location. The emission measurement method/ soil type factor was included to account for the latter dependencies. Because data are observational and there is very little crossed structure within the database, it would be difficult or impossible to separate effects of measurement method and soil type, and we did not try to do so.
Mixed-effects models were fit using the lme() function in the nlme package by maximising the restricted log-likelihood  (Pinheiro et al. , 2016). A few models were compared using Akaike's "An Information Criterion" (AIC)  (Sakamoto et al. , 1986)  using the AIC() function in the stats package (R Core Team, 2017), and in this case, the maximum likelihood method was used for parameter estimation. We focused on three models. Model 1 used absolute cumulative 48 h emission (kg ha -1 as N) as the response variable and all the predictors listed above (13 institutes, 485 observations). Model 2 did not include TAN concentration (to avoid likely spurious responses to TAN and other predictors apparent in model 1) and instead used relative cumulative 48 h emission (fraction of applied TAN) as the response variable (same data subset as model 1).
Model 3 was similar to model 2 but did not include slurry pH, in order to use a larger data subset (14 institutes, 568 observations). All observations with acidified slurry were excluded for model 3. The sensitivity of model coefficients to data from individual institutes was assessed by evaluating the effect of individually removing data from each of the 13 institutes used for model 2 on coefficients. Experiment-based models were used to estimate application method effects from individual data subsets for each experiment. This simple approach provides a method for estimating effects of application techniques free of most assumptions about effects of other variables, and has been applied before  (Huijsmans and Schils, 2009). The response variable was log 10 -transformed 48 h cumulative emission and application method was the only predictor.
Database summary The complete database contained 30907 measurement-interval level observations and 1899 plot-level observations from 1895 plots. In total, 22 institutes from 12 countries contributed data (Table  1 ). Data were strongly unbalanced: cattle manure applied to grassland was best represented in the database (Table  2 ). Most results were for application of cattle and pig manure (1083 plots for cattle and 606 for pig), and other types of manure (mink, poultry, mixed, and "other") and other fertilizers (chemical concentrate and sludge) were much less common. Most data, 1664 plots, were from Europe, with the remainder from the US and Canada.
Variable summary Manure characteristics, application rates, and emission rates all showed large variation within the database, both among and within slurry type (Figs. 1  and 2  show results for cattle and pig manure only). This variation is characteristic of animal production in industrialized countries, and reflects differences in diet and manure management. Dry matter concentration was usually below 5% for pig slurry, and below 10% for cattle, although many observations had higher values. Slurry pH was between 7 and 8 for most observations. A small number of plots from three institutes received acidified slurry (24 plots from institute 205, 9 from institute 210, and 49 from institute 211). Broadcast was the most common application method (927 plots). Trailing hose was used on more than half as many plots (559), while all other methods-trailing shoe, open-and closed-slot injection, band spreading on slots, and pressurised injection-were used on fewer than 150 plots each.
Pressurised injection and band spreading on slots was each done by only a single institute. Micrometeorological measurement of emission was the most common approach on a plot basis (763 plots, including 492 integrated horizontal flux (IHF), 145 ZINST, 62 backwards Lagrangian stochastic model-based (bLS), 21 aerodynamic gradient, and 39 from the original ALFAM database simply identified as "micromet", which included ZINST, IHF, and the perimeter profile method  (Schjoerring et al. , 1992) ), followed by 682 wind tunnel plots. Measurements from calibrated passive samplers  (Gericke et al. , 2011) (318 plots)  were less common than wind tunnel measurements, followed by even fewer chamber measurements (132 plots). Details on measurement methods and other variables can be found in the publications listed in Table  1.
Weather varied widely within the database (Fig. 3 ). Air temperature (24 h weighted averages) ranged from below 0 °C to 29 °C, although extreme values were rare. The most common wind speeds (24 h weighted average of measured values) were between 1 and 3 m s -1 , but maximum values were above 15 m s -1. Rainfall quantity was not recorded for about one-third of all plots (626 of 1895). Nearly half of the remainder (634 of 1269) had zero rain. Interval-level data showed that a majority of intervals (23222 of 30907) had zero rain, possibly reflecting an intentional bias (i. e. , avoiding starting an experiment when rain is expected). Soil bulk density and pH varied widely among plots (Fig. 3 ).
Bussink et al. (1994)  measured reductions in 4 d or 10 d emission of 55%, 72%, and 85% by reducing pH from around 7. 5 to 6. 0, 5. 0, and 4. 5, respectively (some of these data are included in the ALFAM2 database). A focus on experiments where acidification was used may provide more robust estimates of the effect of pH manipulation. But these results may not apply to "natural" differences in pH (when pH was not intentionally manipulated) which may be correlated with other chemical or physical differences among slurries. Effects of slurry pH may also be complicated by interaction with soil, which could be more important for determining pH of slurry on the soil surface after the first few hours. Exclusion of observations with acidified slurry and of slurry pH as a predictor had large effects on several coefficients, with some apparent relative effects changing by a factor of 2 (compare Tables 5 and S3).
Except for open slot injection, none of the coefficients were clearly different from the expected value of unity. The coefficient for open slot injection was not different from zero, but was less than unity, implying that relative emission decreased as TAN concentration increased. Since emission from open slot injection is probably related to the quantity of manure remaining on or close to the surface  (Hansen et al. , 2003) , the lack of a clear effect for injection is plausible. Exclusion of data from individual institutes had large effects for all application methods, although the sign of the effect changed only for open slot injection (Table  S10 ). Slurry TAN concentration varied within a broad range in the database (Figs. 1  and 2 ), but the range was narrow for individual application methods in this data subset, particularly for trailing hose, which may have made it difficult to detect a clear Table  3  Fraction of total (final) emission reached 6, 12, 24, 48, and 72 h after manure application for plots grouped based on total trial duration, for all plots included in the database.
The largest decrease in a coefficient was for log 10 of manure TAN concentration for trailing hose, which decreased by 0. 23 (42%). The pH coefficient for trailing hose showed the largest increase, which was 0. 10 (26%). Coefficients for application methods differed only slightly-the largest difference was for open slot injection, which was lower by 0. 06 (14%) for the 24 h response. These differences highlight both the variability in the effects observed in the data, and the difficulty in estimating effects from a complex observational dataset. A linear response between log-transformed emission and all numeric predictors (possibly log-transformed) was assumed for all models presented in this work. For some variables the true response is known to be more complex. For example, at high dry matter content, cumulative emission has been shown to be insensitive to additional increases, presumably because infiltration is negligible  (Sommer and Olesen, 1991).
(  2012 ) observed a much smaller effect of temperature (1% per °C). Crop height has been shown previously to reduce emission, as reviewed by  Thorman et al. (2008) , who developed regression equations for the effect of both grass and winter wheat height on emission. Their grass equation shows a larger effect of height (-5% per cm). Data used in some of these studies are included in the new ALFAM2 database, and so similarity should not be taken as independent evidence of a particular magnitude of a response. Differences are not surprising, and are reflected in the sensitivity of the coefficients to both the selection of data for model fitting, and the terms that are included. Whether the differences reflect true variability in the response to weather (due, e. g. , to interactions with other variables), measurement bias, site-specific variations in the way a given application method is used, specific differences in application techniques per application method, or random error is unclear.
In the second scenario, biases in emission measurements, determination of accompanying variables, or slurry Fig. 6. Experiment-based comparisons of 48 h relative emission from broadcast and trailing hose application. The x-axis position shows relative emission for broadcast plots, and the relative effect of using trailing hose (change as% of broadcast emission) is shown on the y axis. Labels shows institute codes (Table  1 ). application method explain most of the apparent differences among institutes. Can variables included in the database explain differences among institutes? Differences in emission among soil types within the same institute are large in some cases, providing evidence that soil effects could explain much of the apparent institute effects (Fig. 9 ). Soil hydraulic properties, pH, and the capacity for sorption of NH 4 + could affect NH 3 emission, and earlier studies have also shown large effects of soil properties on emission (e.
g. ,  Sommer et al. , 2006). There is some evidence (Fig. 9 ) that emission from sandy soils is consistently lower than emission from other soils within the same institute (for institutes 106, 210, and 214). But the magnitude of combined random effects does not show a consistent relationship to either soil type or measurement technique across institutes. This may be because soil interactions with slurry are not related solely to soil texture categories. However, it does leave open the possibility that differences in soil explain much of the observed differences among or within institutes. Other variables in the database may have contributed to these observed differences. For example, rain is known to affect NH 3 emission  (Beauchamp et al. , 1982) , but was not included in any models here, since measurements were missing from nearly half the observations in the cattle and grass subset.
Results from two Swiss datasets provide an example of a clear measurement bias. In experiments carried out under comparable conditions with respect to properties of the slurries applied, the experimental sites and meteorological conditions but improved measurement techniques (107: ZINST, 207: bLS), the newer data (institute 207) showed systematically lower NH 3 emission than the older data (institute 107) (Fig. 9 ). Indeed,  Häni et al. (2016)  describe differences between the two datasets in detail, and identified three mechanisms that have contributed a negative bias to the measurements from institute 107 (an incorrect ZINST scaling factor, inaccurate wind speed measurements, and NH 3 (g) transfer among plots), and a note in this ALFAM2 database warns users. But is this result common? The database provides some opportunities to compare results collected within the same country by different groups (204 and 205 from Denmark), and by the same groups at different times (106 and 214 from NL and 105 and 202 from UK).
Results from the Danish institutes suggest that different groups can obtain similar results in a similar location, or not, depending on which model is used to quantify institute effects (Fig. 9 ). (This result highlights the limitations of models built on observational datasets. ) A comparison between Dutch results (institutes 106 and 214) show that older and newer results were similar for organic soil (o in Fig. 9 ) and sand (s), but newer emission measurements for clay (c) are much higher for shallow injection than in older data (Fig. 9 ). This difference may be due to a smaller open slot injection depth in the newer experiments  (Huijsmans and Schils, 2009). Results from the two UK data sets appeared somewhat different for clay soil, which is the only soil type in the later dataset (institute 202).
Measurement method differed here, with higher emission for wind tunnel measurements than IHF. Together these comparisons suggest that differences in slurry application and emission measurement biases could contribute to observed institute differences. A more detailed evaluation of each of these pairs could yield additional insights. In order to understand exactly why results differ so much among individual experiments or institutes, much more information than is included in the database is required (including complete wind and concentration profiles for IHF, and georeferenced data for all plots). If such data cannot be provided a comparison of measurement techniques (and possibly application techniques) at the same location and time may be the only way to definitively assess the contribution of measurement technique biases on results (as suggested by Sintermann et al.
Implications, use, and future of the database The nature of the data in the ALFAM2 database should affect how it is used. It is effectively an observational dataset, and has at least three significant limitations: observations are not independent, data are not balanced, and variables are confounded. The possibility of significant systematic differences among institutes is an important finding of this study and suggests that measures should be taken during data analysis to ensure that conclusions are accurate. Fitting a naive model between one or more predictors and emission without considering correlation among observations from the same institute and effects of other variables will almost certainly result in biased coefficients and probabilities. Omitting random-effect predictors from model 2 (Table  5 ), for example, resulted in different coefficients for application methods, and a statistically significant negative coefficient for slurry pH for trailing hose application, among other differences (Table  S11 ).
Furthermore, data for individual variables may not be consistent among institutes or even experiments. The same levels may have different meanings, and some variables cannot be accurately described in a simple way, e. g. , soil type, where names or texture may not sufficiently characterise interaction with applied slurry. Imbalance in the data means that individual institutes may have large effects on some model coefficients, whether or not the responses present in these data are representative (Table  S10 ). Interactions between variables are almost certainly significant (e. g. , soil moisture and manure dry matter), but the dataset is not close to fully crossed, and effects of these interactions may therefore inflate residuals and make hypothesis testing difficult, or, when confounding is present, contribute to bias or inaccurate effect estimates.
Despite these limitations, the ALFAM2 database is clearly useful. It provides measurements of NH 3 emission from more than 1800 plots across a wide geographic area. These observations may be useful for developing new emission factors or evaluating existing ones. Results presented here suggest that NH 3 emission is affected by multiple factors associated with weather, soil, management, and slurry itself, which vary among experiments and over time. Variation in emission may reflect these effects and interactions, and not necessarily any measurement biases or methodological errors. Careful analysis of these data could yield important and useful information on factors controlling NH 3 emission. Models developed from the ALFAM2 database or similar datasets may have an intrinsic limit on accuracy and precision.
Changes in model coefficients as different data subsets are used (Tables  5, S3 , and S10) strongly suggest that it will be difficult to assess the accuracy of any model with certainty. Although the mixed effects models described above were not developed for making emission predictions (and the inclusion of random-effect predictors limits their utility), they provide some indication of accuracy and precision. Even with random effects for each institute, soil type, and measurement method combination (which improve model fit), these mixed-effects models have large residuals (Fig. 10 ). Root mean square error (an estimate of the average error) is 0. 21 for model 2, which represents a relative value of +62% or -38% of emission. Given the magnitude of unexplained random effects included in this model, it is unlikely that other models (empirical or mechanistic) can perform better with this dataset.
While models presented in other studies may show better performance for smaller (and presumably more homogenous) data sets than the one used here (e. g. ,  Menzi et al. , 1998; Gericke et al. , 2012; Langevin et al. , 2015; Congreves et al. , 2016; Häni et al. , 2016) , they are unlikely to perform better than this empirical model for the wide range of data present in the ALFAM2 database. Expansion of the database will continue, and researchers interested in contributing data should contact the corresponding author for details on data submission. A current version of the database can be found at http://www. alfam. dk. Addition of new measurements may help clarify some questions that have been highlighted in this work, by increasing the variety of measurements and improving balance of the data.
Recommendations It is clear from the discussion above that missing values for some variables in the ALFAM2 database limit its utility. When values are not available for slurry dry matter or pH, for example, it is difficult to determine if an observation is representative of a location, or why two observations differ. To ensure that future results are useful and can be compared or combined with results from other experiments, it will be important for researchers to agree on a list of required variables that should always be reported along with NH 3 emission. As a starting point, we recommend that, at a minimum, the following variables are always measured and reported in NH 3 emission experiments: 1 Soil characteristics: texture, pH, clay and organic matter content, moisture. 2 Soil management: tillage type and timing.
Successful standardisation will require a continued collaborative effort to expand and refine this list. For some of the variables that can be difficult to describe quantitatively or for details that are less commonly documented, digital photographs could be helpful. While storage of the resulting files is not as straightforward as are the data included in the ALFAM2 database, it is possible. And for some variables, additional details may be needed. For example, since it is soil near the surface that interacts the most with applied manure, soil measurements should probably be reported from only a thin layer. Furthermore, additional measurements may be needed for producing high-quality emission measurements. For example, soil moisture alone (volumetric or gravimetric) does little to indicate how important slurry infiltration will be.