dataesr/scientific-paragraphs-categorization huggingface dataset:
20 years. 55The traditional printed bibliographies struggled more with this change. The Internationale ökumenische Bibliographie (IOB), the Bulletin signa létique. Sciences religieuses (Bsig. SR) and even the Elenchus bibliographicus biblicus (EBB) did not make it into the digital age; they all ceased publication. 56The Internationale Zeitschriftenschau für Bibelwissenschaften und Grenzgebiete (IZBG) continued as an E-journal, but it was never integrated into a database and ceased publication in 2011. The Elenchus bibliographicus (ETL. EB) and the bibliography of the Revue d'histoire ecclésiastique (RHE. B) entered the digital age late, but then immediately in the form of online databases. 57In Germany, the Program of the Federal Government for the Support of Information and Documentation ('Programm der Bundesregierung zur Förderung der Information und Dokumentation' or 'IuD-Programm') was launched in the years 1974 to 1977.
58 Within this framework, an academic information system / academic information centre for the humanities (FIS/ FIZ 14) was planned. 59 The FIS/FIZ 14 was to have six decentralised academic departments because of the large number of disciplines assigned to it. Department I (philosophy and theology) "was originally intended to be located The bibliography of the RHE was additionally published by Brepols from 2009 to 2013 as an online database. Subscribers of the printed RHE. B could use the cumulative database, which contained the bibliography of the RHE. B from 1973 on. Information from Dries Vanysacker conveyed by Leo Kenis per e-mail on October 25, 2021. Cf. also F.
58 Within this framework, an academic information system / academic information centre for the humanities (FIS/ FIZ 14) was planned. 59 The FIS/FIZ 14 was to have six decentralised academic departments because of the large number of disciplines assigned to it. Department I (philosophy and theology) "was originally intended to be located The bibliography of the RHE was additionally published by Brepols from 2009 to 2013 as an online database. Subscribers of the printed RHE. B could use the cumulative database, which contained the bibliography of the RHE. B from 1973 on. Information from Dries Vanysacker conveyed by Leo Kenis per e-mail on October 25, 2021. Cf. also F. Keygnaert, E. Louchez, D. Vanysacker, "Bibliographie de la RHE: Introduction," accessed November 7, 2021, https://www. rhe. eu.
ebsco. com/blogs/ebscopost/ celebrating-atla-75-years-scholarship. 56 The IOB ceased to publish in 1992, Bsig. SR in 1994, IZBG and EBB in 2011. The EBB's reviews have also been continuously included in the BILDI database and are now listed in the Index Biblicus and the Index Theologicus through the cooperation between BILDI (Innsbruck) and IxTheo (Tübingen). 57 The ETL. EB was published by Peeters from 2005 to 2013 as an online database. In 2003/2004, there was a CD-ROM version. This information was provided by Gilbert Van Belle and conveyed by Leo Kenis per e-mail on November 9, 2021. Cf. ZDB entry "ETL. EB," ZDB, accessed November 8, 2021, https://zdb-katalog. de/title. xhtml?idn=976795817&view=full. (Cologne: Hochschulschrift, 1998), 26-66. https://doi. org/10. 15496/publikation-64378.
"with 130 subject groups which was hierarchic, allowed the use of truncated search strings, was theoretically expandable and had subject indexing through its class descriptions. "69 This classification allowed the "selection of documents on the basis of broader relationships of thematic, geographical and temporal nature" and constituted a unique selling point on the "market of available theological databases. "70 Tübingen (2003), 4, https://vergil. uni-tuebingen. de/publikationen/10900_43804_fest platte/festplatte/rundgang/pdf/seck1. pdf. 67 Dr. Hilger Weisweiler led the theological department from January 1, 1992 to October 10, 2010. 68 Hilger Weisweiler, THEODOKThesaurus: "Der Zeitschrifteninhaltsdienst Theologie der Universitätsbibliothek Tübingen," Bibliotheksdienst 30, no. 2 (1996): 244, http://dx.
On the history and goals of OpenCitations cf. Silvio Peroni and David Shotton, "OpenCitations: an Infrastructure Organization for Open Scholarship," Quantitative Science Studies 1, no. 1 (2020): 428-444, https://doi. org/10. 1162/qss_a_00023. 127 "Linked Open Citation Database," accessed November 7, 2021, https://locdb. bib. uni -mannheim. de/blog/de/. 128 Timotheus Chang Whae Kim and Martin Faßnacht, "Anwendungsszenarien im Index Theologicus (IxTheo)," lecture held at the second LOC-DB Workshop on November 6, 2018 in Mannheim, accessed November 7, 2021, https://slides. com/timotheuskim/anwendungsszenarien-im-index-theologicus-ixtheo. Digitale.
Phenotypic data collection We previously described the harvest and data collection methods in detail and refer the reader to  Altendorf et al. (2020)  for additional trait descriptions. When flag leaves were fully expanded, width (mm) and length (cm) were measured using a transparent ruler on one representative flag leaf per plant. These numbers were multiplied to obtain area (cm 2 ). Plant height (cm) was measured by gathering the stems, holding them upright, and measuring from the base of the plant to the top of the majority of spikes. Ten uniform, unbroken spikes were harvested from each spaced plant into paper baguette bags and dried at 35 ˚C for 1 wk. The remaining spikes were gathered and clipped ∼10 cm below the peduncle using a large garden shears, and the underside of the bundle was photographed.
Variant detection and filtering We previously described variant detection methods in detail in  Altendorf et al. (2021a). Briefly, genotyping-by-sequencing libraries were created using genomic DNA and a two-enzyme, two-barcode approach  (Zhang et al. , 2016)  and sequenced using Illumina HiSeq 2500. Reads were aligned to the v2 IWG reference genome (access provided by The Thinopyrum intermedium Genome Sequencing Consortium) using Bowtie2  (Langmead & Salzberg, 2012) , and reads that mapped uniquely were analyzed using the Genome Analysis Toolkit v 4. 1. 2 pipeline  (McKenna et al. , 2010). Single-.
Phenotypic data analysis All data analyses were conducted in the program R v4. 0. 3 (R Core Team, 2020). Phenotypic data were fit using linear mixed models using the lme4 package  (Bates et al. , 2014)  with fixed terms for genet nested within-family and block (n = 2) as random. Means and standard errors for fixed terms were calculated as estimated marginal means ('emmeans')  (Lenth, 2018). Broad-(H) and narrow-sense heritabilities (H and ℎ 2 , respectively) were calculated across years separately for STP and TLI, as these growing regions are targeted by separate breeding programs  (Altendorf et al. , 2021b; Zhang et al. , 2016; DeHaan et al. , 2018). Heritability estimates for most traits were reported previously  (Altendorf et al. , 2021b). T tests were used to assess the phenotypic differences between progeny derived from common and donor parents within each cross.
Genome-wide association mapping and linkage mapping Genome-wide association mapping was conducted using GAPIT software MLM model with the default kinship matrix calculation  (Lipka et al. , 2012). Markers with a p value <. 00025 (logarithm of the odds [LOD] = 3. 6) were considered significant. The QTL within peaks were resolved using the 'mmer' function in the R package 'Sommer' (Covarrubias-Pazaran, 2016) as described in  Altendorf et al. (2021a). Allele effects and frequencies were obtained from the GAPIT output. Only GWAS markers identified in at least two of the four environments are reported. The IWG NAM population consensus genetic map was used for LM in MapQTL v6 (Van Ooijen, 2009) using the two-way pseudo testcross model as described in  Altendorf et al. (2021a). The maximum likelihood mixture analysis procedure was used with maximum 20 iterations and with a maximum number of neighboring markers of 10.
A C K N O W L E D G M E N T S This work was supported by the Perennial Agriculture Project in conjunction with the Malone Family Land Preservation Foundation and The Land Institute. We thank Dr. Steven R. Larson for assistance with linkage mapping analyses, Xiaofei Zhang for obtaining the funding for the project and helping to create the NAM population, and Prabin Bajgain for assistance in developing sequencing libraries. Thank you to Brett Heim and Marty Christians for their contributions to data collection and field maintenance. We acknowledge the following individuals for their help in collecting data: Andressa Spuri Azarias, Arthur Martins, Oswaldo Birungi, Caroline Elmer, Phoebe Wanjira, Mario Fagundes, Jennifer LaValley, Charlotte Bonner, and Ellen Goedtke. Finally, we thank the Land Institute Interns of 2017 and 2018 for assistance in the field during data collection trips and harvesting.
Recording neuronal activity with penetrating extracellular multi-channel electrode arrays, more commonly known as neural probes, is one of the most widespread approaches to probe neuronal activity. Despite a plethora of available extracellular probe designs, the time-consuming process of mapping of electrode channel order and relative geometries, as required by spike-sorting software is invariably left to the end-user. Consequently, this manual process is prone to mis-mapping mistakes, which in turn lead to undesirable spike-sorting errors and inefficiencies. Here, we introduce ProbeInterface, an open-source project that aims to unify neural probe metadata descriptions by removing the manual step of probe mapping prior to spike-sorting for the analysis of extracellular neural recordings. ProbeInterface is first of all a Python API, which enables users to create and visualize probes and probe groups at any required complexity level.
Second, ProbeInterface facilitates the generation of comprehensive wiring description in a reproducible fashion for any specific data-acquisition setup, which usually involves the use of a recording probe, a headstage, adapters, and an acquisition system. Third, we collaborate with probe manufacturers to compile an open library of available probes, which can be downloaded at run time using our Python API. Finally, with ProbeInterface we define a file format for probe handling which includes all necessary information for a FAIR probe description and is compatible with and complementary to other open standards in neuroscience.
Currently, however, it is in most cases left to the end user to correctly retrieve the mapping information for their probe from the vendor, to parse the geometric information into file formats that are specific to their chosen spike sorter (e. g. , a. prb file for Klusta, Spyking CIRCUS, a. mat file for Kilosort and Ironclust, etc. ), and to properly verify that the wiring between each recording electrode and the acquired signal is consistent and correct. This last step can be quite tedious and error-prone as most probes require connection to the data-acquisition system via headstages and / or adapters which result in a re-ordering of the signals. In order to address these issues and to make probe handling less error-prone and more reproducible, we introduce ProbeInterface, a Python package for unified and standardized probe handling in neuroscience.
ProbeInterface is first of all a Python API (Application Programming Interface) that enables users to define, visualize, and use probe definitions for their data analysis. Second, ProbeInterface comes with a probe library that was built in concert with Cambridge Neurotech, which is one of the major vendors of extracellular neural probes. Users can download any available probe model from the ProbeInterface library (https://gin. g-node. org/spikeinterface/probeinterface_library) in a single line of code. Third, ProbeInterface allows users to quickly describe the correct wiring between the probe and the recording device through a series of commonly used adapters and headstages. Finally, ProbeInterface comes with a JSON-based file format that carries all relevant metadata about the probe, including the geometry of the electrode (position, shape, size), the intrinsic channel indices, the size of the silicon shanks, and more.
GETTING STARTED WITH PROBEINTERFACE ProbeInterface is a lightweight Python package that can be installed from the Python Package Index (PyPI) (https://pypi. org/project/probeinterface/): The source code is open and available on GitHub (https:// github. com/SpikeInterface/probeinterface) and an extensive documentation with several examples is available on the documentation page (https://probeinterface. readthedocs. io). In this manuscript we are referring to ProbeInterface version 0. 2. 6. A notebook to generate the figures can be found at https://spikeinterface. github. io/blog/probeinterface-paperfigures/. In the following sections, we will show some example use cases using ProbeInterface to create ProbeGroup objects from scratch, to retrieve available commercial probes from the ProbeInterface library, and to wire a group of probes to a specific device, also termed channel mapping.
Using the Probe Library In most cases, neuroscientists use commercially available neural probes. While manufacturers provide the necessary information to use their probes, the probe metadata are typically provided as non-standardized and highly vendor-specific catalogs. During the initial design and implementation of ProbeInterface, we collaborated with one of the major vendors of silicon probes (Cambridge Neurotech, UK) to gather, curate, and distribute most of their available probe designs in a standardized and easy-to-access format. In addition, we are in contact with other vendors (NeuroNexus Technologies, USA, ATLAS Neuroengineering, Belgium) and we have started porting some of their probe designs into ProbeInterface. We have produced a library of probes that is hosted and maintained on the GIN platform (https://gin.
g-node. org/spikeinterface/probeinterface_ library). From the Python API, users can retrieve any available probes as a ProbeInterface Probe object from the abovementioned vendors with a single line of code. In the following example, a Cambridge Neurotech probe (ASSY-156-P-1) and a NeuroNexus probe (A1x32-Poly3-10mm-50-177) are retrieved from the probe library, combined into a probe group, and visualized: Figure  3  shows the output figure, containing the Cambridge Neurotech probe on the left (64 channels distributed over four shanks with 16 contacts each) and the NeuroNexus probe on the right (32 channels organized in three columns of 10, 12, and 10 contacts). Once probe models are downloaded, they are cached by ProbeInterface and do not need to be downloaded again.
, 2018; Buccino and Einevoll, 2020). Given these limitations, we therefore introduce a flexible description based on the established JSON format. The ProbeInterface description of a Probe is an attempt to include all relevant information about the probe(s) used in an experiment. The JSON implementation allows easy access by existing frameworks in different programming languages, from spike sorting tools to visualization and modeling packages. ProbeInterface includes multiple features to ensure its long-term usability and flexibility. First, a ProbeInterface file includes the version of the ProbeInterface package, to ensure reproducibility of the analysis.
prb file, a Neurodata Without Borders (NWB) file  (Teeters et al. , 2015; Rübel et al. , 2021) , and an electrophysiology Brain Imaging Data Structure (BIDS) file  (Gorgolewski et al. , 2016). In addition, each Probe or ProbeGroup object can also be written to a ProbeInterface file, a. prb file, and an ephys BIDS file set.
DISCUSSION We present ProbeInterface, a lightweight and userfriendly Python framework to standardize the description and handling of neural probe mapping for subsequent data analysis. After introducing the main concepts of the ProbeInterface framework (Figure  1 ), we demonstrate how the Python API can be used to create a probe configuration from scratch (Figure  2 ) and to retrieve a commercial probe from the ProbeInterface public library (Figure  3 ). Finally, we describe the file format used by ProbeInterface and how it can interface with other available file formats. Probe description and handling in neuroscientific experiments is far from standardized. The plethora of available probes, connectors, headstages, and acquisition system makes each experimental setup unique. Arising from this, current analysis frameworks mainly start from raw signals, taking the steps that go from the probe to the acquisition system for granted.
With ProbeInterface we aim to improve standardization and reproducibility starting from the underlying signal pathway from probe to computer. By providing an enhanced description of neural probes and automatic mapping between contact and device indices we facilitate the use of this important information by analysis tools. In fact, aspects of the probe design, e. g. , the size and shape of the electrodes, the material, the impedance and coating, arguably influence the quality of the signals and it is, therefore, important to track and include this information in the processing pipeline and data sharing. By collaborating with Cambridge Neurotech, one of the major vendors of neural probes for extracellular electrophysiology, we provide a readily available and validated library of over 100 neural probe designs.
Finally, in addition to standardization of probe handling for analysis pipelines, we are in the process of integrating ProbeInterface objects as extensions to standard file formats employed across the neuroscience community, including Neurodata Without Borders (NWB)  (Teeters et al. , 2015; Rübel et al. , 2021)  and Brain Imaging Data Structure (BIDS)  (Gorgolewski et al. , 2016)  formats. Standardized data formats in neuroscience are arguably improving reproducibility and facilitating data sharing and ProbeInterface integration contributes an user-friendly and comprehensive way to add probe information to these files.
While at a high resolution, multi-platform precipitation products' uncertainty has actively been explored  [10] [11] [12] [13] [14]  at the daily accumulated scale of 1 • × 1 • , less is known in terms of structural errors of the products. Numerous atmospheric processes contribute for modulating the precipitation (detection and intensity) distribution in space and time. Assessing this using satellite, on an 1 • /daily scale, would support research on global precipitation (e. g. , on the investigation of global extremes) and facilitate better intercomparison and validation exercises with a common gridded database. A few validation studies have nevertheless been conducted in the tropics revealing some regionally varying uncertainties that are difficult to interpret in terms of algorithms' limitations  [15].
GIRAFE evolved from the Tropical Amount of Precipitation with an Estimate of ERrors (TAPEER) approach  [19, 23] , which is based on the Universally Adjusted GOES Precipitation Index (UAGPI) technique  [24, 25]. In the present GIRAFE version, the instantaneous rain rate retrievals (i. e. , the L2 input database) were extracted from the Goddard Profiling Algorithm (GPROF2017,  [26] ) and the Precipitation Retrieval and Profiling Scheme (PRPS2019,  [27] ) PMW algorithms were obtained from the GPM V05 and V06 database, respectively. Oliveira et al. [28]  provided a systematic analysis of this source of uncertainty over the last decades and here we illustrated the resulting uncertainty distributions by exploring only 2 configurations that are representative of the actual changes in the constellation setup.
The GPM "golden era" constellation period was selected as a reference for comparison with the other two different configurations, namely (i) an idealized case for a single SSMI/S and (ii) a configuration used to exploit the impact in the absence of MT1. SAPHIR and GPM. GMI. Table  1  shows the PMW radiometers considered for each experimental configuration. Data-denial experiments were performed for June-July-August of 2014 (JJA2014) and December-January-February of 2014/5 (DJF2014/5). The study domain corresponds to a quasi-global zone between 55 • N/S. The GIRAFE framework training volume of 5 • × 5 • × 5 days was adopted for both the intensity and detection parameters, which were assumed as 1. 5 and 0. 5 mm h -1 , respectively. Table  1. The adopted data denial experiments and their respective constellation configurations, based on the number of platforms available for each corresponding period.
Therefore, assuming a multi-platform PMW rain retrieval database, variability in the overall rain distributions and its performances was expected which could also be attributed to the retrieval algorithm  [10, [32] [33] [34] [35]. For instance, according to Chambon et al. [21] , the systematic uncertainties associated with PMW rain retrievals ranging between 2 and 10 mm h -1 lead to an impact on the 1 • /daily precipitation accumulations, resulting in non-Gaussian distributions. In this case, the constellation configuration-induced variability impacted the entire daily accumulated precipitation range. The impact is clearly noticeable through the modification of the RE distribution shape at each precipitation category, which can even be seen in the density spread or peak location.
Acknowledgments: We thank  S. Cloché  for her support with the handling of these various datasets. This study benefited from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  UPMC ,  Labex L-IPSL ,  CNES  and  Ecole Polytechnique. The authors acknowledge the  EUMETSAT  support under the  CMSAF program.
Funding: This study benefited from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  Sorbonne Université ,  UVSQ ,  CNES ,  Ecole Polytechnique  and national research  infrastructures Climeri-France  and DATA TERRA. This study was performed by the  EUMETSAT Satellite Application Facility on Climate Monitoring , and we acknowledge the financial support from the  EUMETSAT  member states through  CM SAF.
Data Availability Statement: The TAPEER data are available at https://www. icare. univ-lille. fr/ megha-tropiques/products/ (accessed on 1 February 2021). The GSMaP Near Real Time V6 Precipitation data were acquired from https://sharaku. eorc. jaxa. jp/GSMaP/ (accessed on 1 February 2021). The GPM IMERG Early Precipitation L3 Half Hourly V06 data were obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC; https://doi. org/ 10. 5067/GPM/IMERG/3B-HH-E/06 (accessed on 1 February 2021)). PERSIANN data were obtained from Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (CHRS, UCI, ftp://persiann. eng. uci. edu/CHRSdata/PERSIANN (accessed on 1 February 2021)). TRMM (TMPA) Rainfall Estimate L3 3 hourly V7 data were obtained from GES DISC (https://doi.
org/10. 5067/TRMM/TMPA/3H/7 (accessed on 1 February 2021)). The CMORPH V1. 0 RAW data are available at ftp://ftp. cpc. ncep. noaa. gov/precip/ (accessed on 1 February 2021). All the figures have been created using the R software package (version 3. 6. 3 for Linux; http: //CRAN. R-project. org/). R Development Core Team. R: A Language and Environment for Statistical Computing. (2008). R package "DistributionOptimization" algorithm is available through the https://cran. r-project. org/package=DistributionOptimization (accessed on 1 February 2021).
Main Superspreading appears a distinct feature of coronavirus transmission during past and recent human outbreaks of novel coronaviruses including the ongoing coronavirus disease 2019 (COVID-  19)  pandemic  (1) (2) (3) (4) (5) (6) (7) (8). The effective reproductive number (Re), and time-varying effective reproduction number (Re t ), are essential epidemiological measures used to quantify rates of pathogen transmission and predict the progression of epidemics, however, these measures often omit heterogeneity in individual level transmission and superspreading effects. Typically, disease transmission is modelled according to a Poisson distribution where individual variance in transmission is equal to Re. However, this distribution is inappropriate when modelling datasets that feature superspreading because the variance greatly exceeds the expected mean.