dataesr/scientific-paragraphs-categorization huggingface dataset:
LC-MS 2 /MS 3 data were collected on an Orbitrap Fusion (Thermo Fisher Scientific) mass spectrometer with acquisition and separation settings. Data were processed using Proteome Discoverer 2. 1 (Thermo Fisher Scientific). MS 2 data were searched against both a public repository of microbial gut genes and the human proteome (www. uniprot. org). The Sequest searching algorithm was used to align spectra to database peptides. A precursor mass tolerance of 50 parts per million (ppm) was specified and 0. 6 Da tolerance for MS 2 fragments. Included in the search parameters was static modification of TMT 10plex tags on lysine and peptide n-termini (þ229. 162932 Da), carbamidomethylation of cysteines (þ57. 02146 Da), and variable oxidation of methionine (þ15. 99492 Da). Raw data were searched at a peptide and protein FDR of 1% using a reverse database search strategy.
A second search was performed taking proteins assigned to either the forward or reverse database into a subset database to increase the spectral match rate within complex data types like the fecal proteome. TMT reporter ion intensities were extracted from MS 3 spectra for quantitative analysis, and signal-to-noise values were used for quantitation. Additional stringent filtering was used to remove any moderate confidence peptide spectral matches or ambiguous peptide spectral match assignments. In addition, any peptides with a spectral interference above 25% were removed, as well as any peptides with an average signal-to-noise ratio less than 10. All signals from peptide spectral matches assigned to the same protein group were summed to represent protein abundance. Protein relative abundances were normalized first to the pooled standards for each protein and then to the median signal across the pooled standard.
An average of these normalizations was used for the next step. To account for slight differences in amounts of protein labeled, these values were then normalized to the median of the entire dataset and reported as final normalized summed signal-to-noise ratios per protein per sample. Hits to microbial proteins were removed to focus analyses toward host proteins. Proteomic datasets generated from inflammatory bowel disease patient samples resulted in final data tables containing 1803 proteins for cohort 1 fecal samples and 2928 proteins for cohort 2 fecal samples. Data analysis was performed in python, and records of the code are available in corresponding Jupyter Notebooks for this project (https://github. com/rhmills/HBOT_ Multiomics). Individual protein abundances from day 1 samples were compared with the abundances of day 10 samples using independent t tests of unequal variance in scipy (www.
scipy. org). Enriched or depleted proteins were determined by p score, which accounts for both fold change and P value. A statistical cutoff for highly ranked associations was set to |p| > 1, as previously performed. Datasets generated for the 2 cohorts of patients were analyzed independently. Human protein gene functional enrichment analysis was performed by using Database for Annotation, Visualization and Integrated Discovery, with all human proteins identified as a background list. The python package, Seaborn (version 0. 9. 0), was used to generate bar plots comparing the -log10 transformed FDR P values provided by Database for Annotation, Visualization and Integrated Discovery for each functional grouping. To account for protein changes related to severity, data were analyzed from a prior project of our group with data available online at www.
These composite scores were determined as increased or decreased more than expected by using a cutoff of |1|. All significant associations were analyzed for protein-protein interactions using networks created through STRING-db. Associations between proteins were determined through default settings, accounting for textmining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence. After identifying a strong association to neutrophil degranulation, proteins specific to this subnetwork were further analyzed. Connections were restricted to interactions between proteins within the query list only. Networks were subsequently visualized through Cytoscape (version 3. 5. 1). Edges within protein networks were based on the combined evidence scores, with the transparency of the edge indicating higher confidence in the association.
0 (arb. units); auxiliary gas heater temperature, 435. 0 C; capillary temperature, 268. 0 C; sheath gas flow rate, 52. 0 (arb. units); spray voltage, 3. 5 kV; and S-lens RF, 50 (arb. units). Positive mode MS data were acquired using a data dependent acquisition method where the 5 most abundant ions are identified and subsequently scanned for MS/MS fragmentation via collision-induced dissociation. MS1 data were collected at a resolution of 35,000 and spanned an m/ z range of 100-1500. MS2 data were collected at a resolution of 17,500. Thermo proprietary ms files(. raw) were converted to a GNPS compatible format (. mzXML) using the ProteoWizard program MSConvert. The open-source software MZmine version 2. 37 was used for feature detection. MZmine modules were used with the following settings.
Akkermansia spp. Targeted Database A set of Akkermansia spp. genomes were selected from NCBI RefSeq and GenBank databases on October 8, 2021. To assess the completeness and contamination of the Akkermansia genomes, we used the lineage-specific workflow from CheckM v1. 1. 02 with the default database. We then performed a quality filtering of genomes (contamination >5% and completeness <90%). Closely related genomes may present a problem for our database because they increase the ambiguity during read classification. Therefore, we identified and removed such genomes. For this, we first estimated the distance between high-quality genomes with Mash v1. 13, testing multiple k-mer sizes. We then performed a hierarchical clustering over the mash distance matrix using the average linkage method, implemented in SciPy v1.
Sequence Alignment and Classification of Akkermansia Strains FASTQ sequences studies 11149 and 12675 were downloaded from the Qiita web server. We aligned all sequences against the updated Web of Life database, containing the high-quality and non-redundant Akkermansia spp. genomes with Bowtie 2 v2. 4. 4. Woltka v0. 1. 3 was used to classify aligned sequences at the species and strain level, filtering features (species and strains) whose abundance in particular samples was below 0. 01% because very low abundance hits are false-positive assignments.
The NWDAF, on the other hand, collects and processes network telemetry data and trains an ML algorithm, so that at later stages, the QoE can be estimated from the KPIs monitored in the network. The studies carried out in this manuscript differ from previous works with respect to several distinct aspects. Instead of estimating the value of specific QoE influence factors, our goal is to estimate the overall MOS score of a completed session. While this has been addressed in some previous works as well, we want to emphasize that we do not make use of classification, but solve a regression problem. Instead of categorizing the QoE into a pre-defined, limited set of classes, we estimate real numbers in the continuous range from 1 to 5. This allows for a wider range of use-cases, including those where the QoE needs to be available on a fine-grained level.
g. , at the UE or the Access Node (AN), which are statistically processed to generate features. Additionally, during this phase, true QoE scores are provided via the AF (denoted as the purple box) from a third party AP. In the course of our practical study, we gather these types of data by means of network simulations 2. Thereby, we consider two distinct applications, i. e. , VoD and VoIP, and take moving as well as stationary clients into account. In this respect, the following question needs to resolved: Which features need to be provided at the NWDAF? Based on our generated data set, we address this question by studying the correlation between the network-related features and the QoE for both service types to elaborate on the application-specific relevance of different features 3. During the model training and testing phase, the logical NWDAF function for training learns the relationship between the network KPIs and the QoE 4.
A. Simulation Scenarios and Ground-Truth Data Collection This section first presents our simulation environment based on the discrete event simulator OMNeT++. Afterwards, we describe the considered applications and the QoE models applied to retrieve the true QoE on MOS scale. Next, the ground-truth data collected within the experiments and the evaluation scenarios are presented. 1) OMNeT++ Simulation Environment: For generating the network-and application-related ground-truth data, we rely on OMNeT++  [16]  and integrate the frameworks INET and SimuLTE  [17]  into our simulation setup. 1 Although SimuLTE simulates 4G networks, the type of monitored information is the same with 5G networks. For instance, the equivalent of 4G Packet Gateway (PGW) and eNB data is information collected from UPF and gNB in 5G.
Our simulations consist of a single AN which serves a varying number of active UEs, which differ with respect to their mobility characteristics. We consider static clients, which do not move throughout the experiments, and moving clients, which walk (3 kmph) or drive (50 kmph) between different points of interest (POIs). To simulate realistic movement patterns, we use the small world in motion (SWIM) movement model  [18]. 1 https://github. com/fg-inet/ML-simulation-and-ground-truth 2) Considered Applications and QoE Models: For each service type, we apply existing QoE models, which allow computing the QoE based on collected network-and/or applicationrelated KPIs. Please note that we assume in this work that the AP provides the QoE, e. g. , on MOS scale, to the MNO. Having this information and the network telemetry data, the MNO can build its own ML-based model, which allows -once the model is trained so to retrieve a reliable estimation -for obtaining the QoE only based on network-related KPIs.
Hence, we can expect a similar performance of the applied ML models, independent of the specific QoE model applied. 3) Collected Ground-Truth Data and Features: With the term ground-truth data, we refer to those data points, where both, network-related information corresponding the VoIP or VoD session and its associated true QoE score are available. While the estimated QoE is the output of the trained ML model, the true QoE is reliably retrieved from the applied QoE models. Our monitored network information for the different application types are summarized in Table  I. All of the monitoring data is collected as time series on a per-second scale granularity. To generate the features, which are the input for the ML models to estimate the QoE, we apply the twelve different statistics shown in the table to the per-session time series.
A. Ground-truth QoE Distribution In order to evaluate the ML performance for different true QoE scores and movement patterns, we divide our groundtruth data set into subsets. We distinguish between stationary and moving clients, as well as between low (M OS < 2. 0), medium (2. 0 ≤ M OS ≤ 3. 5), and high QoE (M OS > 3. 5). Table  III  denotes the number of ground-truth samples and the average MOS values along with their standard deviations for each service type in the different subsets. The average QoE of the VoD clients is higher when using the ITU-T P. 1203 model (2. 18), compared to using CQM (1. 65). With both models, stationary clients obtain a higher QoE than the moving clients. In the moving clients data set, we observe a lower standard deviation compared to that for stationary clients.
ACKNOWLEDGEMENT This work is funded by the  BMBF Software Campus Grant "BigQoE"  ( 01IS17052 ). Shinichi Nakajima  is supported by  BMBF  as  BIFOLD -Berlin Institute  for the Foundations of Learning and Data ( 01IS18025A/01IS18037A ). Thomas Zinner is supported by the  Norwegian Financial Mechanism   2014-2021  under project  2019/34/H/ST6/00599. The authors want to thank  Huiran Liu  und  Marcin Bosk  for their continuous support during the course of this work and  Marija Gajic  and  Stanislav Lange  for proofreading the article.
Paleotemperature proxy data form the cornerstone of paleoclimate research and are integral to understanding the evolution of the Earth system across the Phanerozoic Eon. Here, we present PhanSST, a database containing over 150,000 data points from five proxy systems that can be used to estimate past sea surface temperature. the geochemical data have a near-global spatial distribution and temporally span most of the Phanerozoic. Each proxy value is associated with consistent and queryable metadata fields, including information about the location, age, and taxonomy of the organism from which the data derive. to promote transparency and reproducibility, we include all available published data, regardless of interpreted preservation state or vital effects. However, we also provide expert-assigned diagenetic assessments, ecological and environmental flags, and other proxy-specific fields, which facilitate informed and responsible reuse of the database.
Background & Summary Geochemical proxy temperature data from ancient oceans are a key component of paleoclimate research  [1] [2] [3]. High-resolution paleotemperature data from a single site can identify both long-term  4, 5  and orbital-scale  6, 7  climate variability, while multi-site comparisons from a given time slice provide insight into the spatial patterns of past climate change  [8] [9] [10] [11] [12]. Additionally, such data are essential for validating Earth system models (ESMs)  [13] [14] [15] [16]  and provide critical context for first-order temporal trends in other aspects of the Earth system, including evolution  17, 18  , geochemical cycling  19, 20  , and tectonics  21, 22. While single-proxy data sets that span the Phanerozoic  [23] [24] [25]  and multi-proxy compilations for select time slices  [8] [9] [10] 15  exist, a comprehensive, multi-proxy database of temperature data spanning the Phanerozoic has yet to be published.
PhanSST, a database of sea surface temperature (SST) proxy data spanning the Phanerozoic Eon, seeks to fill this gap. The compilation currently contains 150,691 discrete proxy values, which can be used to estimate past SST. These data come from five different proxies, amassed from 660 references, and represent more than 1,600 unique sampling locations. Each proxy value is associated with a suite of consistent and queryable metadata fields, and the database is available in a machine-readable format. To the best of our knowledge, this is the largest compilation of Phanerozoic paleotemperature proxy data to date. Our intention is to make PhanSST a living database, growing, improving, and evolving through time. Accordingly, we encourage the paleoclimate community to contribute their published data to the compilation going forward.
In addition to the initial purpose of the compilation outlined below, PhanSST is an invaluable resource to the paleoclimate community and can be used for a wide range of additional applications, including evolutionary and geochemical studies. The spatial and temporal reach of PhanSST facilitates paleoclimatic syntheses, including studies investigating climate sensitivity, the biotic and geochemical impacts of climate change, and the mechanisms driving large-scale global climate. The data are presented in their native proxy units, which permits flexibility in the choice of calibration model and additionally enables the compilation to be used in proxy calibration studies. Further, PhanSST includes data from known and suspected diagenetically altered material, with both expert-assigned binary diagenesis flags and additional metadata fields, allowing the database to be used in research investigating spatial and temporal trends in preservation.
Version 0. 0. 1 of PhanSST is presented in a tabular (csv) format. A static copy of Version 0. 0. 1 is archived in the NOAA-NCEI Paleoclimatology Database (https://www. ncei. noaa. gov/access/paleo-search/study/36813)  26 , and dynamic versions of the most recent release can be found on Zenodo (https://doi. org/10. 5281/ zenodo. 7049233)  27 and at the PhanSST website (https://www. paleo-temperature. org). In the following sections, we provide information regarding the data sources and selection criteria, review the definitions and decision-making behind the metadata fields associated with each proxy measurement, and outline the quality control process. We explore the broad spatial and temporal trends of the compilation, discuss future usage and limitations of the data set, and address the ongoing goals for the database to ensure that it remains a valuable asset to the paleoclimate community.
Methods What is the primary purpose of the compilation? The PhanSST database is a part of the broader PhanTASTIC (Phanerozoic Technique Averaged Surface Temperature Integrated Curve) initiative  28  , which aims to produce an internally consistent and statistically robust record of Earth's global mean surface temperature over the last 539 million years. Ultimately, these data will be integrated with ESMs  29  using data assimilation  10, 30  to reconstruct relevant global climate fields and calculate the global mean surface temperature at the geochronologic age (i. e. , stage) level across the Phanerozoic. Requisite to this goal is a compendium of paleotemperature proxy data. PhanSST is a community-wide, collaborative effort. Each of the authors of this data descriptor contributed their time and expertise, entering data and quality control checking the records.
Which SSt proxy data? PhanSST included data from five SST proxies: oxygen isotopes of macro-and microfossil carbonate (δ  18  O carbonate ), oxygen isotopes of conodont phosphate (δ  18  O phosphate ), magnesium to calcium ratios (Mg/Ca) of planktonic foraminifera, the tetraether index of 86 carbons (TEX 86 ), and the alkenone unsaturation ratio (U K 37 ′ ). While we did also collect marine carbonate clumped isotope data (Δ 47 ), those data are not included in the current release (Version 0. 0. 1) of PhanSST. Although laboratory-specific Δ 47 -temperature calibrations are robust, interlaboratory differences in the way Δ 47 data have been corrected raise concerns over the compatibility of the isotope values themselves  40. Since PhanSST presents proxy values, rather than Which metadata fields are included and why? The metadata fields included in PhanSST reflect a balance between the scope of the PhanTASTIC project and our intention to maximize the reuse potential of the database.
Metadata fields were carefully curated to facilitate future updates to age models and proxy-, species-or methodological-specific corrections. For example, core information, sampling depth, and biostratigraphic information are included, permitting age model updates. On the other hand, we do not list age uncertainties because (1) age uncertainties are not consistently reported in the source publications, (2) it was not tractable to update all age models to a consistent timescale, and (3) the PhanTASTIC project focuses on temperature evolution at the stage level. Since we strive to make PhanSST a living database, we anticipate that updated age models, with uncertainties, may be added at a later date with assistance from the paleoclimate community. To ensure a comprehensive and methodologically traceable compilation, we also opted to include diagenetically altered samples.
Which metadata fields are excluded and why? The metadata fields currently provided have been carefully selected to help end users make informed decisions of which data to include in analyses and how best to correct them. However, two key metadata fields are omitted from PhanSST: paleotemperature estimates and paleocoordinates. Though we acknowledge that these fields are of particular interest, this was done deliberately to promote responsible and intentional reuse of the database. Estimating SSTs from the proxy values involves applying and inverting a statistical model of how each proxy responds to environmental parameters; it is therefore a derived or modeled quantity subject to user-based decisions about how to treat each proxy system. In order to estimate SST for each of the more than 150,000 data points, we would need to make executive decisions regarding (1) which calibration to use for each proxy system, (2) what assumptions to use for non-thermal predictor variables (such as seawater chemistry), and (3) which (if any) taxon-specific or analytical corrections to apply.
g. , extracting the local δ  18  O seawater values from an isotope-enabled ESM), such methods cannot be consistently applied across the entire database. And, in fact, the necessary resolution of these assumptions changes based on the scope of the study. Studies, for example, investigating Phanerozoic trends in oxygen isotopes may wish to apply a single calibration to all data points and use simplified assumptions for consistency, whereas studies investigating a specific time slice (e. g. , the Cretaceous), may benefit from taking a more intricate approach (e. g. , using taxa-specific PSMs and drawing δ  18  O seawater values from ESMs). It is therefore not possible for us to calculate SSTs for each data point while applying a traceable, scientifically sound, and ubiquitously appropriate methodology.
Moreover, it is important that those who wish to calculate SSTs from the proxy data within PhanSST be fully aware of the decision making (and uncertainty) associated with the various PSMs and assumptions. Likewise, we do not include paleogeographic information, which is also a derived quantity based on geophysical models. Here, we plot data using the plate rotation model of Scotese and Wright (2018)  707  for illustrative purposes, since it is one of the few rotations that extends back through the Phanerozoic. However, there are a variety of different plate rotation models available, particularly in more recent time intervals, and the projected paleocoordinates can vary significantly based on the rotation model used  38. As with the SST estimates, the choice of which paleorotation is most appropriate to use is dependent upon the application of the data and the temporal resolution of the study.
Given the variety of methods for estimating both SST and paleocoordinates, and the inherent complexity and uncertainty behind those singular discrete values, we believe that it is important for end users to go through the decision making process themselves to ensure that the values are best tailored toward their respective application. We do, however, recognize that users who wish to calculate SSTs or paleocoordinates may benefit from guidance. We offer some broad recommendations on how to select and apply temperature calibrations and rotation models below in the Applying the database in deep time section.
Sample ID and location fields. The first field is the SampleID, which reflects the unique (often alphanumeric) identification code associated with each entry, as originally published. Currently, the SampleID field only applies to samples collected in outcrop; however, in the future, we plan to also include the unique drill core sample IDs. The remaining sample site fields provide information on the geographic and stratigraphic position of each entry. Data that come from drill cores, such as those from IODP, list the SiteName, referencing the expedition site, and also include sampling depth information (i. e. , SiteHole, MBSF, and/or MCD) to allow age models to be updated in the future. Data collected in outcrop similarly include a SiteName and SampleDepth, as well as the geologic Formation from which the data were collected, when available.