dataesr/scientific-paragraphs-categorization huggingface dataset:
For many Earth historians, however, and especially for geochemists (where most of the field's efforts traditionally have focused on analytical measurements rather than data analysis; see  Sperling et al. , 2019) , this frontier requires new outlooks and toolkits. When using compilations to extract global trends through time, it is important to recognize that large datasets can have several inherent issues. Observations may be unevenly distributed temporally and/or spatially, with large stretches of time (e. g. , parts of the Archean Eon) or space (e. g. , much of Africa; Fig. S1 1  ) lacking data. There may also be errors with entries-mislabeled values, transposition issues, and missing metadata can occur in even the most carefully curated compilations. Even if data are pristine, they may span decades of acquisition with evolving techniques, such that both analytical precision and measurement uncertainty are nonuniform across the dataset (Fig.
S2  [see footnote 1]). Careful examination may demonstrate that contemporaneous and co-located observations do not agree. Additionally, data often are not targeted, such that not every entry may be necessary for (or even useful to) answering a particular question. Luckily, these (and other) issues can be addressed through careful processing and analysis, using well-established statistical and computational techniques. Although such techniques have complications of their own (e. g. , a high degree of comfort with programming often is required to run code efficiently), they do provide a way to extract meaningful trends from large datasets. No one lab can generate enough data to cover Earth's history densely enough (i. e. , in time and space), but by leveraging compilations of accumulated knowledge, and using a well-developed computational pipeline, researchers can begin to ascertain a clearer picture of Earth's past.
A PROPOSED WORKFLOW The process of transforming entries in a dataset into meaningful trends requires a series of steps, many with some degree of user decision making. Our proposed workflow is designed with the express intent of removing unfit data while appropriately propagating uncertainties. First, a compiled dataset is made or sourced (Fig. S3 , i. [see footnote 1]). Next, a researcher chooses between in-database analysis and extracting data into another format, such as a text file (Fig. S3 , ii. ). This choice does nothing to the underlying dataits sole function is to recast information into a digital format that the researcher is most comfortable with. Then, a decision must be made about whether to remove entries that are not pertinent to the question at hand (Fig. S3 , iii. ). Using one or more metadata parameters (e.
g. , in the case of rocks, lithological descriptions), researchers can turn large compilations into targeted datasets, which then can be used to answer specific questions without the influence of irrelevant data. Following this gross filtering, researchers must decide between removing outliers or keeping them in the dataset (Fig. S3 , iv. ). Outliers have the potential to drastically skew results in misleading ways. Ascertaining which values are outliers is a non-trivial task, and all choices about outlier exclusion must be clearly described when presenting results. Finally, samples are drawn from the filtered dataset (i. e. , "resampling") using a weighting scheme that seeks to address the spatial and temporal heterogeneities-as well as analytical uncertainties-of the data (Fig. S3 , vi. ).
CASE STUDY: THE SEDIMENTARY GEOCHEMISTRY AND PALEOENVIRONMENTS PROJECT The SGP project seeks to compile sedimentary geochemical data, made up of various analytes (i. e. , components that have been analyzed), from throughout geologic time. We applied our workflow to the SGP database 2 to extract coherent temporal trends in Al 2 O 3 and U from siliciclastic mudstones. Al 2 O 3 is relatively immobile and thus useful for constraining both the provenance and chemical weathering history of ancient sedimentary deposits  (Young and Nesbitt, 1998). Conversely, U is highly sensitive to redox processes. In marine mudstones, U serves as both a local proxy for reducing conditions in the overlying water column (i. e. , authigenic U enrichments only occur under low-oxygen or anoxic conditions and/or very low sedimentation rates; see  Algeo and Li, 2020)  and a global proxy for the areal extent of reducing conditions (i.
e. , the magnitude of authigenic enrichments scales in part with the global redox landscape; see  Partin et al. , 2013). SGP data are stored in a PostgreSQL relational database that currently comprises a total of 82,579 samples (Fig. 1 ). The SGP database was created by merging sample data and geological context information from three separate sources, each with different foci and methods for obtaining the "best guess" age of a sample (i. e. , the interpreted age as well as potential maximum and minimum ages). The first source is direct entry by SGP team members, which focuses primarily on Neoproterozoic-Paleozoic shale samples and has global coverage. Due to the direct involvement of researchers intimately familiar with their sample sets, these data have the most precise (Fig. 1A )-and likely also most accurate-age constraints.
Second, the SGP database has incorporated sedimentary geochemical data from the United States Geological Survey (USGS) National Geochemical Database (NGDB), comprising samples from projects completed between the 1960s and 1990s. These samples, which   (Peters et al. , 2018). Finally, the SGP database includes data from the USGS Global Geochemical Database for Critical Metals in Black Shales project (CMIBS;  Granitto et al. , 2017) , culled to remove ore-deposit related samples. The CMIBS samples predominantly are shales, have global coverage, and span the entirety of Earth's sedimentary record. When possible, the CMIBS data are associated with Macrostrat continuous-time age models; otherwise, the data are assigned age information by SGP team members (albeit without detailed knowledge of regional geology or geologic units).
Next, we converted major elements (e. g. , those that together comprise >95% of Earth's crust or individually >1 wt% of a sample) into their corresponding oxides; if an oxide field did not already exist, or if there was no measurement for a given oxide, the converted value was inserted into the data structure. Then, we assigned both age and measurement uncertainties to the parsed data. In the case of the parsed SGP data, 5,935 samples (i. e. , 7. 1% of the original dataset) lacked an interpreted age and so no uncertainty could be assigned. For the remainder, we calculated an initial absolute age uncertainty by either using the reported maximum and minimum ages: σ = - age a ge 2 maximum m inimum , or, if there were no maximum and minimum age values available, by defaulting to a twosigma value of 6% of the interpreted age: σ = * 0.
In future database projects, there is considerable scope to go beyond this coarse uncertainty quantification strategy. For example, given the detailed metadata associated with each sample in the SGP database, it would be straightforward to develop correction factors or uncertainty estimates for different geochemical methodologies (e. g. , inductively coupled plasma-mass spectrometry [ICP-MS] versus inductively coupled plasma-optical emission spectrometry [ICP-OES], benchtop versus handheld X-ray fluorescence spectrometry [XRF], etc. ). Correcting data for biases introduced during measurement is common in large Earth science datasets  (Chan et al. , 2019). However, such corrections previously have not been attempted in sedimentary geochemistry datasets. Next, we processed the data through a simple lithology filter because, in the general case of rock-based datasets, only lithologies relevant to the question(s) at hand provide meaningful information.
The choice of valid lithologies (or, for that matter, any other filterable metadata) are dependent on the researchers' question(s). As highlighted in the Discussion section, lithology filtering has significant implications for redox-sensitive and/or mobile/immobile elements. In this case study, our aim was to only sample data generated from siliciclastic mudstones. To decide which values to screen by, we manually examined a list made up of all unique lithologies in the dataset. We excluded samples that did not match our list of chosen lithologies (removing ~63. 5% of the data; Table  S1 ; Fig. S4  [see footnote 1]). Our strategy ensured that we only included mudstones sensu lato (see  Potter et al. , 2005 , for a general description) where the lithology was coded. Alternative methods-such as choosing samples based on an Al cutoff value (e.
g. ,  Reinhard et al. , 2017) -likely would result in a set comprising both mudstone and nonmudstone coded lithologies. In the future, improved machine learning algorithms, designed to classify unknown samples based on their elemental composition, may provide a more sophisticated means by which to generate the largest possible dataset of lithologyappropriate samples. We then completed a preliminary screening of the lithology filtered samples by checking if extant analyte values were outside of physically possible bounds (e. g. , individual oxides with wt% less than 0 or greater than 100), and, if so, setting them to NaN. Next, to reduce the number of mudstone samples with detrital or authigenic carbonate and phosphatic mineral phases, we excluded samples with greater than 10 wt% Ca and/or more than 1 wt% P 2 O 5 (removing ~66.
9% of the remaining data; Fig. S4  [see footnote 1]). Additionally, in order to ensure that our mudstone samples were not subject to secondary enrichment processes, such as ore mineralization, we queried the USGS NGDB to extract the recorded characteristics of every sample with an associated USGS NGDB identifier. We examined these characteristics for the presence of selected strings (i. e. , "mineralized," "mineralization present," "unknown mineralization," and "radioactive") and excluded any sample exhibiting one or more strings. Finally, as there were still several apparent outliers in the dataset, we manually examined the log histograms of each element and oxide of interest. On each histogram, we demarcated the 0. 5th and 99. 5th percentile bounds of the data, then visually studied those histograms to exclude "outlier populations," or samples located both well outside those percentile bounds and not part of a continuum of values (removing ~5.
Data Resampling We implemented resampling based on inverse distance weighting (after  Keller and Schoene, 2012) , in which samples closer together-that is, with respect to a metric such as age or spatial distance-are considered to be more alike than samples that are further apart. The inverse weighting of an individual point, x, is based on the basic form: ( )= y x d x x 1 ( , ) i p , where d is a distance function, x i is a second sample, and p, which is greater than 0, is a power parameter. In the case of the SGP data, we used two distance functions, spatial (s) and temporal (  t  For n samples, the proximity value w assigned to each sample x is: ∑ ( ) ( ) ( )= + + + = = w x s t 1 1 1 1 i i n 1 2 2. Essentially, the proximity value is a summation of the reciprocals of the distance measures made for each pair of the sample and a single other datum from the dataset.
Then, we culled the dataset into an m by n matrix, where each row corresponded to a sample and each column to an analyte. We resampled this culled dataset 10,000 times using a three-step process: (1) we drew samples, using calculated P values, with replacement (i. e. , each draw considered all available samples, regardless of whether a sample had already been drawn); (2) we multiplied the assigned uncertainties discussed above by a random draw from a normal distribution (µ = 0; s = 1) to produce an error value; and (3) we added these newly calculated errors to the drawn temporal and analytical values. Finally, we binned and plotted the resampled data. Naturally, the reader may ask how we chose the values for scale age and scale temporal and what, if any, impact those choices had on the final results? Nominally, the values of scale age and scale temporal are controlled by the size and age, respectively, of the features that are being sampled.
RESULTS To study the impact of our methodology, we present results for two geochemical components, U and Al 2 O 3 (Fig. 2 ). Contentswise, the U and Al 2 O 3 data in the SGP database contain extreme outliers. Many of these outliers were removed using the lithology and Ca or P 2 O 5 screening (Figs. 2A  and 2C ); the final outlier filtering strategy discussed above handled any remaining values of concern. In the case of U, our multi-step filtering reduced the range of concentrations by three orders of magnitude, from 0-500,000 ppm to 0-500 ppm.
DISCUSSION The illustrative examples we have presented have implications for understanding Earth's history. Al 2 O 3 contents of ancient mudstones appear relatively stable over the Moving forward, there is no reason to believe that the compilation and collection of published data, whether in a semi-automated (e. g. , SGP) or automated (e. g. , GeoDeepDive;  Peters et al. , 2014 ) manner, will slow and/or stop  (Bai et al. , 2017). Those interested in Earth's history-as collected in large compilations-should understand how to extract meaningful trends from these ever-evolving datasets. By presenting a workflow that is purposefully general and must be adapted before use, we hope to elucidate the various aspects that must be considered when processing large volumes of data. Foremost to any interpretation of a quantitative dataset is an assessment of uncertainty.
, 2016). An age without an uncertainty is not a meaningful datum. Indeed, such a value is even worse than an absence of data, for it is actively misleading. Consequently, assessment of age uncertainty is one of the most important, yet underappreciated, components of building accurate temporal trends from large datasets. Of course, age is not the only uncertain aspect of samples in compiled datasets, and researchers should seek to account for as many inherent uncertainties as possible. Here, we propagate uncertainty by using a resampling methodology that incorporates information about space, time, and measurement error. Our chosen methodology-which is by no means the only option available to researchers studying large datasets-has the benefit of preventing one location or time range from dominating the resulting trend.
For example, although the Archean records of Al 2 O 3 and U especially are sparse (Fig. 2 ), resampling prevents the appearance of artificial "steps" when transitioning from times with little data to instances of (relatively) robust sampling (e. g. , see the resampled record of Al 2 O 3 between 4000 and 3000 Ma). Therefore, researchers should examine their selected methodologies to ensure that: (1) uncertainties are accounted for, and (2) that spatiotemporal heterogeneities are addressed appropriately. Even with careful uncertainty propagation, datasets must also be filtered to keep outliers from affecting the results. It is important to note that the act of filtering does not mean that the filtered data are necessarily "bad," just that they do not meaningfully contribute to the question at hand.
For example, while our lithology and outlier filtering methods removed most U data because they were inappropriate for reconstructing trends in mudstone geochemistry through time, that same data would be especially useful for other questions, such as determining the variability of heat production within shales. This sort of filtering is a fixture of scientific researche. g. , geochemists will consider whether samples are diagenetically altered when measuring them for isotopic data-and, likewise, should be viewed as a necessary step in the analysis of large datasets. As our workflow demonstrates, filtering often requires multiple steps, some automatic (e. g. , cutoffs that exclude vast amounts of data in one fell swoop or algorithms to determine the "outlierness" of data; see  Ptáček et al.
, 2020)  and others manual (e. g. , examining source literature to determine whether an anomalous value is, in fact, meaningful). Each procedure, along with any assumptions and/or justifications, must be documented clearly (and code included and/or stored in a publicly accessible repository) by researchers so that others may reproduce their results and/ or build upon their conclusions with increasingly larger datasets. Along with documentation of data processing, filtering, and sampling, it is important for researchers also to leverage sensitivity analyses to understand how parameter choices may impact resulting trends. Here, through the analysis of various spatial and temporal parameter values, we demonstrate that, while the spread of data varies based on the prescribed values of scale spatial and scale temporal , the averaged resampled trend does not (Fig.
CONCLUSIONS Large datasets can provide increasingly valuable insights into the ancient Earth system. However, to extract meaningful trends, these datasets must be cultivated, curated, and processed with an emphasis on data quality, uncertainty propagation, and transparency. Charles Darwin once noted that the "natural geological record [is] a history of the world imperfectly kept"  (Darwin, 1859, p. 310) , a reality that is the result of both geological and sociological causes. But while the data are biased, they also are tractable. As we have demonstrated here, the challenges of dealing with this imperfect record-and, by extension, the large datasets that document it-certainly are surmountable.
1500 Ma (the time for which appreciable data exist in our dataset), suggesting little first-order change in Al 2 O 3 delivery to sedimentary basins over time. The U contents of mudstones shows a substantial increase between the Proterozoic and Phanerozoic. Although we have not accounted for the redox state of the overlying water column, these results broadly recapitulate the trends seen in a previous much smaller (and non-weighted) dataset (Partin et al. , 2013)  and generally may indicate oxygenation of the oceans within the Phanerozoic.
This solution, called the end-toend training in the literature  [8] , guarantees that frames whose references have been through CNN-based QE are present in the training set. However, since it is important that the references of such frames are also enhanced by the same CNN-based QE, the end-to-end training solution has a potential chicken-andegg problem. One way to overcome the above problem is to run the dataset generation step and the training step in multiple iterations. Starting from the first temporal layer, in each iteration, one temporal layer is used for training and a network is trained for it. Then, all frames in that layer are enhanced in the dataset generation step, to be used in the training step of the next temporal layer. This solution is extremely time-consuming, therefore not practical for the current problem.
A. Experimental setup 1) Dataset: The training phase has been carried out under the recent Deep Neural Network Video Coding (DNNVC) Common Test Conditions (CTCs), released by JVET  [71, 72]. The recommended dataset in these CTCs is BVI-DVC  [73] , which consists of 800 videos of 10-bit pixel representation, in different resolutions covering formats from CIF to 4K. We also used two image databases, namely DIV2K and Flickr2K for the training of intra-based networks. These datasets are composed of 900 and 2650 high quality images, respectively. The videos and images in the training dataset were converted to 10-bit YCbCr 4:2:0 and only the luma component has been used for training. To train the models for inter frames, the native Random Access (RA) configuration of VTM10. 0 reference software was used with input and internal pixel depth of 10-bit.
Moreover, all in-loop filters were kept activated. The video dataset was encoded in five base QPs, {22, 27, 32, 37, 42}. For each QP, four out of 64 frames of each reconstructed video were randomly selected. Finally, a total of 3200 reconstructed frames obtained for each QP base, resulting in 16000 frames for all five QPs. The equivalent ground truth and prediction signal, as well as QP map for these reconstructed frames, were also extracted. The networks for intra frames were trained separately by encoding the DIV2K and Flickr2K datasets in the All Intra (AI) configuration. In total, 3550 images were generated, from which we randomly selected 1200 for each QP, resulting in 5800 images. Moreover, we added the intra frames of the RA dataset to the AI dataset. To sum up, a total of 7400 intra frames were used.
Finally, a patch-based strategy was employed which will be explained in Section V-A2. For the test phase, nineteen sequences from the JVET CTCs (classes A1, A2, B, C, D and E) were used  [74]. It is important to note that none of these sequences were included in the training dataset. The test sequences were finally encoded in RA and AI configurations, using the same encoder settings as for the training. 2) Training Settings: The networks were implemented in PyTorch platform and the training was performed on NVIDIA GeForce GTX 1080Ti GPU. The parameter N (number of residual blocks of the network) was set to 16. All networks were trained offline before encoding. The initial learning rate was set to 10 -5 with a decay of 0. 5 for every 100 epochs. The Adam optimizer  [75]  was used for back propagation during the training and each network was trained for 500 epochs.
The validation dataset was extracted from the training dataset and was composed of 50 cropped reconstructed frames and their corresponding prediction and original frames. During the training, the best network parameters were chosen based on the evaluation performed on the evaluation dataset. The training has been performed on 64 × 64 patches, randomly chosen from the training dataset. These patches are fed to the network on batches with a size of 16. Block rotation and flip were also applied randomly to selected patches to achieve data augmentation. It is important to note that one single model is shared between the three colour components (Y , U and V ) in all QP values. Given that the proposed method requires different models to apply the block-level coding type mask (see Section III-C), the following models should be stored at the encoder and decoder sides: • Intra-trained model, for enhancing intra frames as well as intra blocks in inter frames.
Introduction The usefulness of terminology extraction from corpora is clearly acknowledged as it has generated a great deal of research and discussion. This well-established process is used in natural language processing and has led to the development of several tailored tools such as TBXTools  [31] , TermSuite  [9] , BioTex  [22] , etc. Based on  [22] , our proposal deals with domain-based terminology extraction from heterogeneous corpora, and how to efficiently generate a quantitative and qualitative analysis. To this end, we propose a generic methodology hinged on a combination of extraction and analysis strategies. Term extraction strategies are based on combinations of linguistic, statistical measures, and corpus segmentation approaches, while analysis strategies are based on combinations of extracted terms.
Based on the combined strategies, ITEXT-BIO aims to extract: (1) representative terms, (2) discriminant or relevant terms, and (3) new relevant terms from a corpus or corpora. These strategies are specifically useful for dedicated tasks, such as corpus analysis, specific domain monitoring (e. g. epidemiology) or scientific research monitoring. This paper is organized as follows. In Section Related work, we briefly present the state-of-the-art related to terminology extraction. Section Dataset description details the dataset dedicated to scientific papers. Sections Methodology and Experiments respectively provide an overview of our proposal and the experiments. In Section Case study: epidemic intelligence, we illustrate the genericity of the proposal by presenting a case study of an implementation of the combined strategies for epidemiological intelligence analysis.