dataesr/scientific-paragraphs-categorization huggingface dataset:
Financial support. Heiko Goelzer  has received funding from the programme of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Dutch Ministry of Education, Culture and Science (OCW) , under grant no. 024. 002. 001. For NCAR-CISM this material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under cooperative agreement no. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://doi. org/10. 5065/D6RX99HX),were provided by the  Computational and Information Systems Laboratory  (CISL) at  NCAR. Alice Barthel  was supported by the  US Department of Energy (DOE) Office of Science Regional and Global Model Analysis (RGMA)  component of the Earth and Environmental System Modeling (EESM) programme ( HiLAT-RASM  project) and the  DOE Office of Science (Biological and Environmental Research)   Early Career Research programme.
Philippe Huybrechts  and  Sébastien Le  clec'h acknowledge support from the  iceMOD  project funded by the  Research Foundation -Flanders (FWO-Vlaanderen). Ralf Greve  and Chris Chambers were supported by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  (grant no. JP16H02224 ). Ralf Greve  was supported by  JSPS KAK-ENHI  (grant no. JP17H06104 ) and by the  Arctic Challenge for Sustainability (ArCS)  project of the  Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)  under programme grant no. JPMXD1300000000. Reinhard Calov  was funded by the  Bundesministerium für Bildung und Forschung  (BMBF; grants PalMod-1. 1 and PalMod-1. 3). Support for  Nicholas Golledge  and  Daniel Lowry  was provided by the  New Zealand Ministry of Business Innovation and Employment  (contract  RTVU1705 ).
Martin Rückamp  received support from the  Helmholtz Climate Initiative REKLIM (Regional Climate Change ). The AWI-ISSM simulations were run on the  DKRZ HPC system Mistral  under grant  ab1073. Jonathan Gregory  and  Robin S. Smith  were supported by the  National Centre for Atmospheric Science , funded by the  UK National Environment Research Council. Work was performed by  Nicole Schlegel  and  Helene Seroussi  at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. Funding was provided by grants from the  Jet Propulsion Laboratory Research Technology and Development programme. Sophie Nowicki ,  Erika Simon ,  Isabel Nias ,  Helene Seroussi  and  Nicole Schlegel  were supported by the  NASA programmes Cryosphere Sciences ;  Sea Level Change Team ; and Modeling, Analysis, and  Prediction.
Code availability. Data processing, analysis and plotting scripts are available on GitHub (https://github. com/ismip, last access: 15 August 2020) and are archived in permanent repositories on Zenodo with digital object identifiers https://doi. org/10. 5281/zenodo. 3939115 (Goelzer, 2020a)  and https://doi. org/10. 5281/zenodo. 3939113 (Goelzer, 2020b). Data availability. The recomputed scalar variables analysed in this paper are available on Zenodo with digital object identifier https://doi. org/10. 5281/zenodo. 3939037 (Goelzer, 2020c). The forcing datasets are available through the ISMIP6 wiki (http://www. climate-cryosphere. org/wiki/index. php?title=ISMIP6_wiki_page,.
Especially repeatability is pooled over several experimental units. In case of one parameter and repeatability, the analysis can be performed using simple spreadsheet-software like MS Excel. It is also possible to pool the results over several samples and use fewer replicates within the factors, however, homogeneity of variances must be achieved for the analysis then, eg. by transformation of the measurement values (ln, square root). One should note that the CI-approach (which would use the one-sided upper confidence limit here) is not common in precision evaluations in the laboratory medicine community. It was shown that the level of variability was mainly related to the size of the population. Accordingly, When investigating measurement uncertainty, it must be considered that the various cytometric stains used are not independent variables.
14. Robustness, specificity: When measurements of distorted and not-distorted samples must be compared, it is the aim to show a missing difference. As introduced and explained above, the TOST can be used to show the equivalence. Depending on the design, paired or unpaired measurements must be regarded, whereby a paired design is more powerful. Beside other software, free of cost MS Excel-tools are available (https://www. acomed-statistik. de/ en-gb/statistical-tools-download. html#TOST). The sample size depends on width of interval included by acceptance criteria, the expected real difference and its standard deviation as well as on the assumed α (typically 5% and β errors (typically 10-20%). The following Table  5  provides sample sizes for a paired design (all samples are measured under both conditions; the difference of both results is evaluated in analysis).
ACKNOWLEDGMENTS The authors would like to thank the scientific community supporting them. CL, FP, GY, and US are members of the European Society of Clinical Cell Analysis (ESCCA) working group on Primary Immunodeficiencies. CL, KP, MS, and US are members of the IFCC working group on flow cytometry. MÖ, TK, and US contribute to the working group Entscheidungslimits/Richtwerte of the DGKL. MÖ and US are consultants of accreditation bodies. The authors acknowledge support from the  German Research Foundation (DFG)  and  Leipzig University  within the  program of Open Access Publishing.
Subjects 63 TD patients and 34 gender, age and educational level matched healthy controls (HC) were included in the study in an approximate 2:1 ratio. TD patients were recruited through the Tourette reference center at the Pitié-Salpêtrière Hospital in Paris. All participants gave their written consent to participate in the study, and obtained a financial compensation (50€ per subject plus transport compensation for patients). The study was approved by the ethics committee (CCP16163/C16-07) and preregistered prior to the research being conducted on ClinicalTrial at https://clinicaltrials. gov/ct2/show/NCT02960698. The study procedures did not deviate from the preregistered protocol. Following data quality inspection (i. e. incomplete or non-exploitable behavioural or neuroimaging data), 13 TD patients and 8 HC were excluded.
This ability to inhibit an already initiated action could be described as a race between the go and stop systems  (Logan et al. , 1984). The first of two systems reaching a specific threshold wins the race and performs (go winner) or inhibits (stop winner) an action. Indeed, the SSRT is a measure of reactive motor impulsivity. We estimated SSRT using the integration method  (Logan et al. , 1984) , which was recently recommended  (Verbruggen et al. , 2019). Two different SSRT were calculated for each participant according to the emotional condition (low for neutral pictures and medium for both positive and negative images). In addition, data inspection was performed to assess inhibition curves related to the SSD. Participants were excluded from analyses if the curves did not reach a proportion of 20% failed inhibition, and if they were incoherent (i.
Neuroimaging data acquisition and analyses Neuroimaging data were acquired on 3T MRI scans (Siemens Prisma, Germany) with a 64channel head coil. Two sequences were acquired: a T1-weighted image (MP2RAGE, TR=5s, TI=700/2500ms, fov=256, 1mm isotropic, Ipat acceleration of 3) and a resting-state functional MRI (rs-fMRI) of 11min duration. Rs-fMRI data were acquired with the eyes opened: the subjects were asked to fixate on a cross during the sequence acquisition, which was monitored by an eye tracker. Multi-echo echo-planar imaging (EPI) sequences were performed with a multi-slice, multi-echo acquisition scheme (TR=1. 9s, TE=17/36/56ms, Ipat acceleration factor 2, Multi-band 2, isotropic voxel size 3mm, dimensions=66x66 in plane x 46 slices). T1-weighted images were first denoised  (O'Brien et al.
, 2014)  and pre-processed (segmentation, normalisation to Montreal Neurological Institute space and smoothing at 10mm) using the Computational Anatomy Toolbox (http://www. neuro. uni-jena. de/cat/) extension from SPM12 (www. fil. ion. ucl. ac. uk/spm/). EPIs were processed with the MEICA toolbox (www. github. com/ME-ICA/me-ica/). This toolbox implements standard pre-processing steps: slice timing correction and realignment to the first volume driven by the first echo, coregistration to the anatomic volume. A single warp was applied to combine realignment and coregistration. Then, we performed a principal component analysis to reduce the dimensionality of the dataset by removing thermal noise, and an independent component analysis decomposition to separate BOLD (blood-oxygenlevel dependent) from non-BOLD components based on the echo time dependence of the BOLD component  (Kundu et al.
, 2013; Kundu, Inati, Evans, Luh, & Bandettini, 2012). After the independent component analysis, we obtained a dataset where thermal noise and physiological noise such as movements, respiration artefacts, and cardiac artefacts were removed. Prior to analysis, to control for movements artefacts, we performed statistical analyses on movements metrics and found no group (HC vs. TD) difference for the mean framewise displacement (p=0. 89), the standard deviation of the framewise displacement (p=0. 92), the maximal value of framewise displacement (p=0. 93) and the root mean square values (p=0. 97). Finally, we normalized the dataset using the flow field generated by the T1 processing from CAT12. We performed correlation analyses of behavioral performance only on the group of patients presenting motor inhibition impairment.
Data availability statement The conditions of our ethics approval do not permit public archiving of individual anonymised raw data. Readers seeking access to the data should contact the lead author Dr. Worbe. Access will be granted to named individuals in accordance with ethical procedures governing the reuse of sensitive data. Specifically, requestors must obtain a specific authorization from the ethics committee. Regarding E-prime and R scripts for experimental task and statistical analyses, they are available at https://osf. io/k48c9/.
g. , mean absolute error (MAE), root mean square error, and Pearson's correlation] and the different datasets used for evaluation. The Predictive Analytics Competition (PAC) 2019 challenge offers a unique opportunity to benchmark algorithms, techniques (e. g. , data augmentation), and image processing options by offering a common framework to all research groups. In short, the test set was not accessible to the participants to avoid overfitting and data leakage, and prediction accuracy was assessed using MAE, while a secondary challenge aimed at also minimizing bias (defined as the association between PAD and chronological age). With the use of the data proposed for the PAC challenge, a previous publication reported a MAE of about 4-5 years  (6, 22) , in line with the best results reported in the literature at the time  (4).
We used the OSCA software  (35)  to estimate the BLUP scores. The model used in BLUP calculation also allows estimating the total (linear) association between a trait and features (coined morphometricity), which represents the upper bound of the (linear) prediction accuracy that may be achieved from the data  (28, 36). We scaled the scores using the mean and SD calculated from each site of the discovery sample (BLUP-mean). In addition, and to better account for the non-normal distribution of age in the PAC sample, we also applied a quantile-based scaling by which we forced the predicted age distribution to match that of the training sample (BLUP-quantiles).
Model 2: Support Vector Regression We used SVM  (37)  with a radial basis function kernel. SVM is a popular machine learning algorithm that was first introduced to address binary classification tasks  (38)  and then extended to regression tasks. The regression version has yielded successful applications in numerous fields, including time series prediction  (39) , energy forecasting  (40, 41) , recognition  (42) , and medicine  (43). We used the implementation provided in the Python package scikit-learn  (44).
DISCUSSION Here, we describe the ensemble learning of seven different age predictions from T1w MRI images, which led to a MAE of 3. 33 years on an independent dataset, held by the organizers of the PAC2019. From all worldwide competitors, our prediction ranked third, though we only narrowly beat teams ranking fourth (MAE = 3. 33) and fifth (MAE = 3. 37). To note, the gap was more consequent with teams who ranked first (MAE = 2. 90), second (MAE = 3. 09), or sixth (MAE = 3. 55). In absence of reported SE in the PAC results, we cannot conclude whether the different prediction accuracies are statistically different from each other. It is important to keep in mind that ranking of prediction accuracy may be highly dependent on the metric chosen as well as on the test data  (55). Statistical testing can provide a confident ranking of algorithms, and inclusion of other datasets is needed to conclude about the generalizability (and performance) of the prediction scores on samples with other demographics, MRI machines, or patient groups for instance.
Despite the improvement, the performance of BLUP-mean and SVM was still significantly lower than that of Inception V1. To note, the difference between BLUPquantiles and Inception V1 became non-significant. Here, we compared two competing approaches of processing T1w MRIs, implemented in two software suites [FreeSurfer (25) and SPM]. Each processing stream allows multiple user-defined options (e. g. , on registration, normalization, and templates) whose effect on age prediction is not known. Importantly, the image processing maximizing age prediction may not be the best suited to predict another phenotype (e. g. , disease status). Lastly, the good performance of BLUP-median on gray matter maps raises the question of cost-efficiency and updatability of prediction, considering that deep learning models require about 24 h of computing on a GPU, while BLUP only takes a few minutes on a single CPU.
Our attempt to re-train part of the network on adults above 40 years of age (age specialized six-layer CNN) was not conclusive in improving the age prediction accuracy. Other avenues for research include enriching the training sample in specific age groups or demographics that show a lower performance. We did not find error or absolute error to be associated with sex or site, despite differences in global head size, or site differences in term of scanners, demographics, and image qualities. An investigation on a larger dataset may be more powered in detecting subgroups with larger MAE. To finish on bias, we found that rescaling the scores using the median and median absolute deviation (per site) could reduce drastically the bias but resulted in an increase in MAE (Table  1  and Supplementary Tables  10, 11 ).
Past research in the region allows for thorough assessment of LULCC from a multidisciplinary point of view, confronting us with complex deforestation and land-use patterns. We leverage structure-from-motion to generate a large orthomosaic of historical imagery and to develop a convolutional neural network-based forest cover mapping approach based upon a semi-supervised generated dataset extensively leveraging data augmentation. Our methodology aims to provide a historical insight into important LULCC spatial patterns in Yangambi, such as fragmentation and edge complexity. We further contextualize the influence of changes in the forest's life history on past and current research into Aboveground Carbon (AGC) storage  [30]  and biodiversity  [12]  in the central Congo Basin. Our fast scalable mapping approach for historical aerial survey data, using limited supervised input, would further support long-term land-use and land-cover change analysis across the central Congo Basin.
Site Selection We prioritised flight paths and images that contained current-day permanent sampling plots, larger protected areas, and past agricultural and forest research facilities (Figure  2 ). This selection provides a comprehensive mapping of the Yangambi area and the life history of the forest surrounding it. Thereafter, we selected flight paths 1 through 11 for digitization. From this larger dataset of 334 images, we selected 74 survey images for orthomosaic compositing and further analysis. All the selected images stem from the flight campaign made during January and February of 1958. The area includes the Yangambi village, 20 contemporary permanent sampling plots  [30] , past and present agricultural experimental plots  [32] , and large sections of the Yangambi UNESCO Man and Biosphere reserve surrounding the west and east sides of the village.
Digitization and Data Processing All selected images covering the Yangambi area were contact prints as original negatives of the prints were not available. Images were scanned at a resolution exceeding their original resolution (or grain) at the maximal physical resolution of an Epson A3 flatbed scanner (i. e. , 2400 dpi or 160 MP per image) and saved as lossless tiff images. Data were normalized using contrast limited histogram equalization  [33]  with a window size of 32 and a clip limit of 1. 5. Fiduciary marks were used to rectify and downsample the images into square 7700 × 7700 pixel images (~1200 dpi, 81 MP). This resulted in a dataset with digital images at a resolution that remained above the visible grain of the photographs, whilst the reduced image size facilitated easier file handling and processing speed.
Separate polygons were selected for training, testing, and validation purposes. Validation polygons were sampled 300 times, while both testing and validation polygons were sampled at 100 random locations. Tiles extracted from locations close to the polygon border at times contained mixed cover types. Tiles with mixed cover types were removed from the list of source tiles (Table  1 ). Homogeneous source tiles were combined in synthetic landscapes using a random Gaussian field-based binary mask (Figure  4 ). We generated 5000 synthetic landscapes (balancing forest and non-forest classes) for training, while 500 landscapes were generated for both the validation and the testing datasets for a total of 6000 synthetic landscapes. In order to limit stitch line misclassifications, along the seams of mosaicked images, we created synthetic landscapes with different forest tiles to mimick forest texture transitions.
Model Validation We report the CNN accuracy based upon the IoU of our out-of-sample validation dataset of synthetic landscapes. In addition, we report confusion matrices for all pixels across the homogeneous validation polygons as well as the training and testing polygons (see Figure  3 ). Furthermore, we used the first acquisition of a recent panchromatic Geo-Eye 1 stereo pair (Geo-Eye, Thornton, Colorado, U. S. A. , order 737537, 2011-11-11 08:55 GMT or 09:55 local time) to classify and assess the robustness of the CNN algorithm on contemporary remote sensing imagery with similar spectral and spatial characteristics. We used the Global Forest Change version 1. 6 (GFC, tile 10N-020E)  [1]  map data, capturing deforestation up to 2011, to quantify accuracy on downsampled CNN Geo-Eye classification results.
Characterizing Long-Term Change To map long-term LULCC in the Yangambi region, we used the contemporary Global Forest Change version 1. 6 (GFC, tile 10N-020E) (lossyear) map data  [1]. Using the GFC data, we calculated the latest state of the forest with respect to the conditions at the start of 1958, 60 years earlier. In our analysis, we only included GFC pixels which recorded no forest loss throughout the whole 2000-2018 period. Forest loss in the context of GFC is defined as "a stand-replacement disturbance or a change from a forest to non-forest state". As such, locations which would see reforestation or deforestation between 2000 and 2018 would be marked as non-forest (i. e. , disturbed). As the resolution of the historical forest classification exceeds that of the GFC map, we downsampled our historical forest cover data to 30 m GFC resolution using a nearest neighbour approach.
We masked out all water bodies using the Global Forest Change survey data mask layer and limited the analysis to the right bank of the Congo river. We provide summary statistics of historical and contemporary deforestation and reforestation. We map permanent deforestation after 1958, reforestation after 1958, recent deforestation, and long-term (stable) forest cover. All references to changes over time in the context of our analysis explicitly compare the historical and contemporary periods from hereon forth.
Statistics were calculated for all forest disturbance patches larger than 1 ha and smaller than the 95th percentile of the patch size distribution using the R package "landscapemetrics"  [43]. We provide mean and standard deviation on edge, area, their ratio, and fractal dimension for both the historical and contemporary Hansen et al. [1]  forest cover maps. We estimated aboveground carbon (AGC) losses and gains due to deforestation and reforestation using plot-based averages of recent inventory data at permanent sampling plots in the area (Figure  2 ). We refer to Kearsley et al. [30]  for the survey method and allometric relations used to scale the survey data. Unlike standard square 1 ha plots, edge plots (163. 03 ± 19. 39 Mg C ha -1 , N = 5) were set back 200 m from forest edges and were 50 × 200 m, with the 50 m side of the plot along the forest edge and continuing 200 m into the forest (Appendix A Table  A2 ).
Data Recovery Challenges In our study, the archive data recovered was limited to contact prints and therefore did not represent the true resolution of the original negative. In addition, analogue photography clearly produces a distinct softness compared to digital imagery (Figure  8 ). Despite favourable nadir image acquistions  [51] , image softness combined with illumination effects between flight paths and the self-similar nature of vast canopy expanses  [52] [53] [54]  limited our ability to provide wall-to-wall coverage of the entire dataset containing 334 images. A few man-made features in the scenes also made georeferencing challenging. Although the village of Yangambi provided a range of buildings as (hard-edge) references, other areas within the central Congo Basin might have fewer permanent structures and would require the use of soft-edged landscape features (e.
Scaling Opportunities Our approach uses broadly defined homogeneous polygons to construct a balanced dataset of synthethic landscapes. The methodology is analoguous to the use of sparse labelling as used by Buscombe and Ritchie  [56]  and contrasts with the standard methodologies which generally extract pixel (windows)  [22]  or delineate land cover classes  [23]  to drive a classifier or analysis. More so, the use of heavy image augmentation during model training sidesteps texture representation issues which affect classification of image scenes with inconsistent illumination or sharpness  [25]  or ad hoc feature engineering  [22]. The use of synthetic landscapes allowed us to account for most, but not all, of the variability within our orthomosaic. Our analysis has shown that, despite being trained on historical data, our model could map contemporary forest cover in remote sensing data with similar spatial and spectral characteristics (Figure  7 ), suggesting that the classifier consistently works across both space and time.