dataesr/scientific-paragraphs-categorization huggingface dataset:
5  confirms that our modeling of noise clusters is accurate. To exclude such events from the WIMP search, we fit the simulated distribution with an exponential function and set a value of ΔLL < -22 to ensure that there are fewer than 0. 1 noise events expected in our dataset. This selection leads to a reconstruction efficiency < 10% below 50 eV ee , which we set as our analysis threshold. In addition, we apply the following quality criteria; we remove clusters containing or adjacent to a masked pixel, clusters that span more than one row, clusters too close to each other on an image such that their fitting windows overlap, and clusters where the maximum pixel contains less than 20% of the total charge (indicating a charge distribution that is too broad to arise from a pointlike event). The efficiency of these cuts is shown in Fig.
A. Material assays All materials used in the construction of DAMIC at SNOLAB were carefully catalogued and assayed for longlived radioactivity. These assays focused primarily on the primordial 238 U and 232 Th chains, but also measured 40 K in the detector materials. For the 238 U chain, we consider the possibility that two different segments of this chain, delimited by 226 Ra (τ 1=2 ¼ 1. 6 kyr) and 210 Pb (τ 1=2 ¼ 22 yr), may be out of secular equilibrium with 238 U and with each other. A full list of the assay results of the materials used in the DAMIC at SNOLAB detector can be found in Table  I , and are detailed below. For many of the materials, assays were performed using either Inductively-Coupled Mass Spectrometry (ICP-MS) FIG. 5. Distribution of the log-likelihood ratio of clusters from simulated noise images (black) compared to CCD data (red).
The simulated distribution is fit (dashed line) to extract the ΔLL selection to reject noise clusters. The excess events in the tail of the data distribution above the expectation from noise correspond to low-energy ionization events. or Glow Discharge Mass Spectrometry (GDMS). These techniques measure the elemental composition of a sample and estimate the activity of a specific isotope from its natural abundance. We adopt the standard values 1 Bq=kg 238 U per 81 ppb U, 1 Bq=kg 232 Th per 246 ppb Th, and 1 Bq=kg 40 K per 32. 3 ppm K  [43]. All materials constrained by mass spectrometry are indicated as such in Table  I  with a superscript M. Most materials used in the DAMIC at SNOLAB detector were screened by Germanium γ-ray spectrometry at the SNOLAB γ-ray counting facility  [44]. This method nondestructively measures the isotopic abundance from the intensity of specific γ lines from radioactive decays in the sample.
All isotopes considered are shown in Table  II. DAMIC simulation code developed within GEANT4 outputs the precise energy and position coordinates of each interaction in our CCDs. We process the GEANT4 raw outputs into a compressed format that retains all information for event reconstruction. This is done event-by-event for each simulated decay by taking each energy deposition and converting it into some number of electrons, assuming 3. 8 eV ee =e -and a Fano factor F ¼ 0. 129  [39, 70]. The electrons are then distributed according to our diffusion model (Sec. II C) on a grid where each cell is the size of a CCD pixel. For energy depositions in the partial charge collection region (Sec. III F), we additionally assign some probability based on the depth of the interaction that a simulated electron will recombine.
A custom software package is then used to convert this reduced, pixelated GEANT4 output into an analogous output from a CCD image. To do this, we rebin to match our 1 × 100 image format, add white readout noise to each pixel, and simulate pixel saturation by setting a maximum energy per pixel based on the measured image pedestal values and CCD calibration constants. Then, we run the "fast clustering" algorithm, detailed in Sec. II C, on the simulated pixels, which allows us to compress many terabytes of simulated data into a list of reconstructed cluster variables including E, σ x , and mean ðx; yÞ for the simulated cluster. We additionally carry through information about the simulated collected energy E sim , obtained by multiplying the simulated number of collected electrons by 3. 8 eV ee , and the mean depth z of each event.
IV. BACKGROUND MODEL For comparison with data, our understanding of radioactive background sources and detector response must be converted into a background model: the expected distribution of ionization events in our CCDs in energy and depth. We construct the background model from a fit to data from CCDs 2-7 above 6 keV ee with templates generated from our GEANT4 simulation output. We validate the fit result with CCD 1 above 6 keV ee and demonstrate consistency with independent estimates of 210 Pb surface activity from individual event identification. The background model is extrapolated below 6 keV ee for our WIMP search.
We reserve all data from CCD 1 as a further cross check, since the background environment in this CCD is different from CCDs 2-7. We group like-simulations together according to common materials and decay chains. The grouping for materials is according to the detector parts in Table  I , with further subdivision of the copper into the OFHC copper vessel, box, and modules (because of their different activation histories), and the EFCu CCD 1 module. All EFCu is assumed to be perfectly radiopure, as it contributes ≪ 1 count kg -1 day -1 keV ee -1. For each detector part, we further group simulated isotopes by decay chain, according to Table  II. To avoid introducing uncertainty from the limited statistics of subdominant components, we discard any simulations that result in fewer than 0. 1 events kg -1 day -1 and less than 1000 simulated events reaching the CCDs, leaving 289 simulations.
In total, 3 (4) bins are removed for CCDs 2-7 (CCD 1), which account for a total rate of 0. 07 ð0. 16Þ events=kg-day, or less than one event in the combined exposure. The resulting background model template for CCDs 2-7 in simulated coordinates is shown in Fig. 10. In order to maximize our resolution at this stage, we choose fine binning of 10 eV ee in E sim and 15 μm in z. The background model has a bulk background rate of 3. 1 AE 0. 6 ð6. 5 AE 0. 4Þ counts kg -1 day -1 keV ee -1 between 2. 5 keV ee -7. 5 keV ee in CCD 1 (CCDs 2-7), where the error bars provided are statistical, and an implicit energy threshold set by GEANT4 of ∼10 eV ee. 5  In Sec. IVA, we constructed the background model using a fit to only data from CCDs 2-7, excluding data from CCD 1. CCD 1 is treated separately since the overall background rates are roughly 50% lower than in CCDs 2-7, primarily because of the EFCu module and additional ancient lead shield.
Using simulated events for CCD 1 and the best-fit parameters C l , we construct an analogous FIG. 10. The background model template (for CCDs 2-7) in raw simulated energy E sim and depth (z ¼ 0 corresponds to the front of the CCD) according to the best-fit combination of the 49 templates used in the binned likelihood fit above 6 keV ee. The color bar indicates the rate of events expected per kg-day per 50 eV ee × 15 μm bin. 4 The observation of 210 Po α's from the copper does not imply a presence of 210 Pb on the copper surface since the copper cleaning procedure is known to be more efficient in removing 210 Pb than 210 Po  [60]. 5  The Livermore physics list is validated down to 250 eV  [75, 76] , and we independently extend this validation down to 50 eV ee using our data  [67]. background model for CCD 1.
18 keV -1=2 Þ. We treat the positively valued amplitude of the additive exponential as a free parameter in our WIMP-search fit. We perform this procedure for the CCD 1 and CCDs 2-7 background models separately and find that the exponential component in CCD 1 corresponds to the same fraction of events in the highest z bin as in CCDs 2-7, but its amplitude is 25% smaller due to the lower background rates in CCD 1 external to the sensor. Thus, to reduce the number of free parameters in our WIMP-search fit, we fix the ratio of the additive exponential component between CCDs 2-7 and CCD 1 to 75%. We perform the WIMP-search fit on events identified by the "likelihood clustering" algorithm presented in Sec. II C, which is designed to efficiently identify low-energy clusters on full CCD images. Thus, we need to translate the final background model in simulated coordinates into reconstructed coordinates by the likelihood clustering algorithm, correctly accounting for all reconstruction efficiencies in the data down to our analysis threshold of 50 eV ee.
We sample from the background model (both the lower bound and the additive exponential component) separately for CCDs 2-7 and CCD 1 to obtain E sim and z values for simulated events. We then apply the CCD-response simulation (diffusion, binning, saturation, etc. ) and add the pixel values from the events randomly onto blank images. To best represent the data, we also add shot noise from leakage current to the simulated images at the levels measured for the exposed images. A total of 830 simulated images with 250 randomly sampled events each (for a total of 200,000 simulated events per CCD) is then run through the same analysis pipeline as the data (including masking, likelihood clustering and cluster selection) to obtain the final simulated clusters and their reconstructed coordinates. Unlike the analysis that is performed to translate simulated GEANT4 events into reconstructed clusters in Sec.
The energy bins below 200 eV ee show a clear excess above background with a spectrum that is well described by the best-fit generic signal model. Furthermore, we confirm that events with energies below 200 eV ee are distributed uniformly in ðx; yÞ and time, as expected for a dark matter signal. Several systematic tests were performed to confirm the consistency of our fit result, with results summarized in Table  IV : (a) If we perform the fit to events with energies > 200 eV ee only, the result is consistent with the background-only hypothesis and shows no preference for a generic signal. (b) If we perform the fit to the data from CCD 1 or CCDs 2-7 separately, the resulting generic signal is statistically consistent between the two datasets with a higher statistical significance in CCD 1, which has the lowest background.
VI. CONCLUSION In this paper, we present the first comprehensive background model for a CCD detector down to an unprecedentedly low energy of 50 eV ee. The background model shows excellent agreement with the data down to 200 eV ee , with an unexpected and statistically significant excess of events at the lowest energies discussed in Sec. V. In the process of constructing this background model, we compiled and determined the relative contributions of various radioactive background sources. We note that our dominant background (see Table  III ) comes from cosmogenic 3 H in the bulk of our CCDs and 210 Pb deposited on the surfaces of the CCDs, including the surface of the wafer prior to CCD fabrication, now embedded ∼2. 5 μm beneath the CCD back surface. The measured activity of bulk 3 H from the template fit allows us to determine (according to the exposure history of our CCDs) the activation rate of cosmogenic 3 H in silicon.
For every sample, we compute the total Pearson's χ 2 and the corresponding contribution from each bin relative to the best-fit background model PDF. The left-hand side panels of Fig. 19  show the distribution of total Pearson's χ 2 for the fake data samples with results for CCDs 2-7 (CCD 1) in the top (bottom) panel. The corresponding result from the fit to the data is marked by the vertical dashed line. The right-hand side panels show the distribution of the contribution of bins to the Pearson's χ 2 averaged over all fake data samples compared to the distribution from the best-fit to data. Overall, we find excellent agreement between the generated samples and the data in CCD 1. For CCDs 2-7, three statistically outlier data bins result in a mismatch between data and the fake samples; one event with E ∈ ½18.
ACKNOWLEDGMENTS We are grateful to SNOLAB and its staff for support through underground space, logistical and technical services. SNOLAB operations are supported by the  Canada Foundation for Innovation and the Province of Ontario Ministry of Research and Innovation , with underground access provided by  Vale  at the Creighton mine site. We acknowledge financial support from the following agencies and organizations:  National Science Foundation through Grants No. NSF   PHY-1912766  and  NSF   PHY-1806974 ;  Kavli Institute for Cosmological Physics at The University of Chicago  through an endowment from the  Kavli Foundation ;  Gordon and Betty Moore Foundation  through Grant  GBMF6210  to the  University of Washington ;  Fermi National Accelerator Laboratory  (Contract No. DE-AC02-07CH11359 );  Institut Lagrange de Paris Laboratoire d'Excellence  (under Reference No.
ANR-10-LABX-63 ) supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program  under Reference No. ANR-11-IDEX-0004-02 ;  Swiss National Science Foundation  through Grant No. 200021_153654  and via the  Swiss Canton of Zurich ; Project  PID2019-109829GB-I00  funded by  MCIN/ AEI /10. 13039/501100011033 ;Mexico's  Consejo Nacional de Ciencia y Tecnología  (Grant No. 240666 ) and  Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica  Grants No. IB100413  and No. IN112213 );  STFC Global Challenges Research Fund  ( Foundation Awards Grant   ST/R002908/1 ).
Background The 2000s and the sequencing of complete genomes sparked a scientific revolution in the study of living beings. The now accessible no a priori approach results in the wider spread of high-throughput experimental techniques such as transcriptomics, proteomics, metabolomics, or phenomics and an increase in the volume of publicly available data. As a consequence, biologists are accumulating large amounts of datasets which are characterised by an increasing heterogeneity: • information sources heterogeneity -multiple databanks, which can be local or distant, with various formats and interfaces, multiple file formats, • data heterogeneity -various scales (from molecules to populations), types (quantitative and qualitative), modes (text or images), and structuring levels (database fields, structured text, free text).
Therefore the demand by biologists to integrate heterogeneous and large datasets from "omics" and phenotyping activities is rapidly expanding  [1]. In this context where large complex datasets are becoming increasingly widespread, biologists often rely on multivariate analysis techniques to project individuals into a new coordinate space to get a first insight into the data and have smaller matrices to process. However, such approaches are not always easily achievable, in particular when faced with mixed (qualitative and quantitative) incomplete (that is to say, including missing values) datasets. Moreover, displaying large numbers of individuals leads to cluttered visualisations, with occlusions, which are difficult to interpret. In this paper, we introduce a new methodology designed to overcome these limitations.
The approach relies on a new semantic distance which is designed for both quantitative and qualitative variables and allows for a realistic representation of the relationships between individuals. This semantic distance is based on ontologies which are engineered to represent real-life knowledge regarding the underlying variables. We associate this new distance definition with an archetype concept to overcome the cluttered displays issue. We define archetypes as individuals representing groups of similar individuals from the dataset. Limiting the visualisations to these archetypes leads to a sparser representation which still provides valuable insight into the data. More precisely, the structuring of the population in groups is conducted through clustering, for which numerous approaches exist  [2, 3].
A common characteristic of clustering techniques is that they group individuals based on their similarity. This similarity is estimated based on distances between the features of the individuals. However, most clustering methods rely on numeric arithmetic. Therefore the features have to be represented by numeric values. This causes problems with qualitative variables and even more in the case of mixed datasets. Some distances are designed to cope with qualitative data, for instance Jaccard's coefficient  [4] , Dice's coefficient  [5] , Gower's distance  [6] , or the Chi-Square  [7]. These metrics are widely used in biology, and in particular in ecology, to characterise species populations. For example Pandey et al rely on Jaccard's coefficient to cluster sesame (Sesamum indicum L. ) populations  [8] , Pavoine and colleagues extend Gower's distance to characterise periurban woodland plant species populations  [9].
But a lot of qualitative variables modalities are structured according to more elaborate schemes: it would be possible to describe such variables as ontologies  [13]. Ontologies structure knowledge as graphs. Nodes represent concepts or terms and edges represent relationships between them. Ontologies are heavily developed and used in life sciences to annotate data, in particular in almost every biological database, and reason over domain knowledge  [14]. In an ontology representing the modalities of a variable, modalities/values could be viewed as concepts. The complex links between these modalities would be materialised by the graph of relationships between concepts. We therefore propose to use the distance between concepts in corresponding ontologies to measure the distance between modalities of qualitative variables.
A classical ontological distance calculation would lead to identical pairwise distances between these countries. This is false from a geographical point of view: Italy is closer to France than Denmark. Therefore context dependent definitions for distances between modalities of a variable should be used: • distance calculation algorithms in the corresponding ontology, in particular when the graph is large, • distances based on real-life information such as in the geographical example above, • ontology graphs augmented with expert-provided distance values associated with the relations between concepts. Moreover, clustering and distance calculations usually cannot be performed as is on datasets including missing data. But data matrices in biology are often incomplete, for example because of the cost of some experimental techniques or because an individual hasn't been available for the whole study.
The first approach to coping with missing data is to exclude the affected individuals and variables. In our case study, this approach is inapplicable due to a high percentage of missing values. More than half the rows contain missing values and four out of eleven variables have more than 84% of missing values. A second approach relies on estimating missing values using imputation techniques. A review of these methods is available in  [18]  in the field of epidemiology. However, using imputation techniques depends on the data at hand. In trait datasets, Johnson and colleagues  [19]  show that estimating missing data is not always appropriate. For our case study, the traits are sometimes different between individuals of the same category. Therefore, we chose to ignore the missing values and to define a distance based on the available data only.
Use case: rosebushes phenotypic traits We illustrate our study with information on rosebush varieties associated to the DNA collection of the Pome Fruits and Roses Biological Resource Centre (BRC) in Angers, France. The dataset consists in passport and phenotypic data assessed and/or gathered from various sources during the study of French roses (Rosa sp. ) performed by Liorzou et al. [22]. It includes 1434 rosebushes from European garden roses from the 18 th and 19 th centuries. Most of these rosebushes correspond to different varieties. Each rosebush is described by the variables listed in Table  1. All the variables are qualitative, except for the number of flowers, and field experts defined all the modalities. The dataset is far from complete. The "Quantity of prickles", "Perfume intensity", "Repeat flowering level" and "Number of Passport variables come from  [22]  or are inferred from them.
The horticulture group is defined according to the American Rose Society (ARS) classification. Breeding dates are grouped into time periods. Phenotypic variables have been evaluated by the Pome Fruits and Roses BRC and its partners: breeders and rose gardens flowers by volume" variables are only known for a small number of rosebushes. Only one or two variables are known for some of the individuals. Such a dataset could be difficult to analyse with classical approaches. We chose it to test whether our method could provide new insight into the data. The dataset was therefore subjected to the pipeline presented in Fig. 1. This pipeline was developed in Python 3. 7. It relies mainly on the NumPy  [23]  and pandas  [24]  libraries to manipulate the data, scikit-learn  [25]  to perform machine learning, and matplotlib  [26]  and seaborn  [27]  to draw the figures.
General principle In the first stages of the process, we associated each qualitative variable in the dataset with an ontology. This corresponds to steps (1) and (2) from the pipeline in Fig. 1. The various modalities of a variable then became concepts in an ontology. If an ontology corresponding to the variable was publicly available, we used and adapted it to fit our variable modalities as necessary. Otherwise we had to rely on expert knowledge to transform the list of modalities into the concept graph of an ontology. We have to define a pairwise distance for each pair of concepts in each ontology, as indicated in step (3) of the pipeline in Fig. 1. If the distance between modalities can be calculated based on what they actually represent, as is the case for geographical areas, we use that calculation.
If the ontology graph is small enough, we can consider augmenting it using expert-provided distance estimations which will be stored along with the ontology graph. Otherwise, when the graph is large or if no extra expert knowledge is required, we fall back to using existing algorithms to calculate a distance between concepts. According to these principles the qualitative variables in our dataset were handled as follows, as for step (4) of Fig. 1. Fig. 1  The dataset processing pipeline. Based on the list of qualitative variables we define the list of required ontologies (one for each variable). For each ontology, if relevant data are publicly available we retrieve it. Otherwise, we rely on expert knowledge to build the ontology graph. We introduce distances between concepts in the ontologies based either on real-life distances or expert knowledge.
These ontologies including distance between concepts information are used to build a distance matrix between variable modalities for each qualitative variable. Based on the vector of variable values which represent each individual in the dataset we calculate pairwise distances to build a distance matrix between individuals. Individuals are then projected in a coordinate space using dimension reduction methods. Individuals coordinates are used during the clustering process to build groups. Representative individuals for each group are estimated to define the groups'archetypes which are used as part of the visualisations.