dataesr/scientific-paragraphs-categorization huggingface dataset:
All normalised datasets except bayNorm and the Scaling method have been divided by the < β > value used in bayNorm procedure. For this analysis smFish data were normalised for variation in total transcript numbers using either cell size measurements (Grn study) or expression levels of a house keeping gene (Torre study) as detailed in Supplementary note 3. 2 (d) Differential expression analysis using data from Soumillon study  [4]. Ten samples of 20, 50, 80, 100, 200 or 400 cells were selected randomly from two groups of stage-3 differentiated cells at day 0 (D3T0) or day 7 (D3T7). DE detection was performed between groups as described at the top of the figure using a list of DE genes obtained from matched bulk RNA-seq data as a benchmark (1000 genes with the largest magnitude of log fold-change between the D3T0 and D3T7 samples)  [3].
Methods 1 The Bayesian model used in bayNorm A scRNAseq dataset is typically represented in a matrix of dimension P × Q, where P denotes the total number of genes observed and Q denotes the total number of cells studied. The element x ij (i ∈ {1, 2,. , P } and j ∈ {1, 2,. , Q}) in the matrix represents the number of transcripts reported for the i th gene in the j th cell. This is equal to the total number of sequencing reads mapping to that gene in that cell for a non-UMI protocol. For UMI based protocols this is equal to the number of individual UMIs mapping to each gene  [5, 6]. The matrix can include data from different groups or batches of cells, representing different biological conditions. This can be represented as a vector of labels for the cell groups or conditions (C j ). A common approach for normalizing scRNAseq data is based on the use of a global scaling factor (s j ), ignoring any gene specific biases (for a recent review see  [7] ).
The normalized data xij is obtained by dividing the raw data for each cell j by the its global scaling factor s j : xij = x ij s j (1) In bayNorm, we implement global scaling using a Bayesian approach. We assume given the original number of transcripts in the cell (x 0 ij ), the number of transcripts observed (x ij ) follows a Binomial model with probability β j  [1] , which we refer to as capture effeiciency and it represents the probability of original transcripts in the cell to be observed. In addition, we assume that the original number or true count of the i th gene in the j th cell (x 0 ij ) follows Negative Binomial distribution with parameters mean (µ), size (or dispersion parameter, φ), such that: Pr(x 0 ij = n|φ i , µ i ) = Γ(n + φ i ) Γ(φ i )n! ( φ i µ i + φ i ) φi ( µ i µ i + φ i ) n So, overall we have the following model: x ij ∼ Binom(x 0 ij , prob = β j ) x 0 ij ∼ NB(mean = µ i , size = φ i ) (2) Using the Bayes rule, we have the following posterior distribution of original number of mRNAs for each gene in each cell: Pr(x 0 ij |x ij , β j , µ i , φ i ) Posterior = Likelihood Pr(x ij |x 0 ij , β j ) × Prior Pr(x 0 ij |µ i , φ i ) Pr(x ij |µ i , φ i , β j ) Marginal likelihood (3) The prior parameters µ and φ of each gene were estimated using an empirical Bayesian method as discussed in detail in Section 4 below.
Estimation of capture efficiencies Cell specific capture efficiency β j and global scaling factor (s j ) are closely related. We can transform scaling factors estimated by different methods (see below) into β j values with the following formula: β j = (s j /s) β (13) β, a scalar, is an estimate of global mean capture efficiency across all cells, which ranges between 0 and 1. There are two different methods for estimating β and β j : 1. If spike-ins or smFISH data are available they can be used to estimate capture efficiencies. We can either divide the total number of observed spik-ins in each cell by the total number of input spike-ins, or we can fit a linear regression  [1]  to estimate the cell specific β j. If smFISH data is available, we can fit a linear regression between the mean expression of raw data (response variable) and the mean expression of the smFISH data (explanatory variable).
The coefficient of the explanatory variable can be used as β  [13]. 2. The raw data itself can be directly used for estimation of cell specific global scaling factors (s j ). Then equation 13 and an estimate of β can be used to estimate β j. There are different methods available for estimation of global scaling factors. Some were developed for bulk RNAseq data  [14, 15]  and some are specific to scRNA-seq data  [16, 17]. The value of β depends on the protocol used and can be batch dependent. For example, for Droplet based protocol, it is about 0. 06  [1]  or 0. 12  [18]. β can also be estimated by spike-ins or smFISH data as explained above. We finally note, that estimates of capture efficiency discussed above will assume cells have simular original transcript content. Therefore, the bayNorm outputs estimates of original transcript counts for a typical cell, which is corrected for variation in cell size and transcript content.
Cells are grouped together for prior estimation, based on cell-specific attributes (C j ). Prior estimation can be done over all cells irrespective of the experimental condition. We refer to this procedure as "global". Alternatively, suppose that there are multiple groups of cells in the datasets and we have reasons to believe each group could behave differently. Then we can estimate the prior parameters "µ and φ" within each group respectively (within groups with the same C j value). We refer to this procedure as "local". Estimating prior parameters across a certain group of cells based on "global" procedure allow for removing potential batch effects. Multiple groups normalization based on "local" procedure allows for amplifying the inter-groups' differences while mitigating the intra-group's variability, which is suitable for DE detection.
Code availability The R package bayNorm is available at https://github. com/WT215/bayNorm. The codes for producing figures in the paper are provided at https://github. com/WT215/bayNorm_ papercode. In the Bacher study, the code for running MAST and log fold change calculation was kindly provided by Rhonda Bacher, the author of SCnorm  [20]. In the Torre study, the code for transforming counts per million normalized data to UMI data was kindly provided by Mo Huang, the author of SAVER  [9].
For estimating β, scaling factors were estimated using scran  [2]  and were normalized to 0. 03 (see Methods). The impact of different β can be found in Supplementary Figure  S20  b. Data was used in the Figure  3c , Supplementary figures: S9e-f, S10e and S20b. Patel study (non-UMI). Raw data were stored in the R package "patel2014gliohuman"https://github. com/willtownes/ patel2014gliohuman  [7]. Single cell data were scaled by a factor 20 and rounded as this is a non-UMI data and the scaled and rounded raw data is closer to the theoretical dropout vs mean curve. Cells with total counts less than or higher than the tenth percentile of total counts across cells were filtered out. In addition, genes with mean expression less than 1 were also removed. β were estimated using scran  [2]  with parameter "positive=TRUE".
The size factors were normalized to 0. 06 (see Methods). Finally, cells with β < 0. 01 were filtered out, leaving 590 cells and 5519 genes in the final datasets. We applied bayNorm on the scaled, rounded and preprocessed dataset. The estimated priors were used as input for Binomial bayNorm simulation protocol (Supplementary Figure  S10f ). Data was used in the Supplementary figures: S10f.
Tung study (UMI). Filtered molecule count matrix as well as the code for estimating β using spike-ins were downloaded from the GitHub repository: https://github. com/jdblischak/singleCellSeq [8]. The list of benchmark DE genes was kindly provided by the author of R package DECENT  [3]. Genes with 0 values across all three individuals were filtered out leaving 13058 genes in the final dataset. No cells were filtered out, resulting in 142, 201 and 221 cells for individuals NA19098, NA19101 and NA19239 respectively. Data was used in the Figure  4 , Supplementary figures: S3-5, S8c-d, S10b-d and S22-23.
Grün study (UMI). Single-cell RNA-seq expression data were downloaded from GEO GSE54695  [9]. The smFISH data used in that paper was kindly provided by the author. The downloaded data was transformed to transcript number. We adapted the code provided in the supplementary material of  [10]  to convert the data to UMI count. We followed the same criterion as  [10]  for filtering genes in the 2i and serum data respectively. After filtering, we kept 74 cells and 9377 genes for the 2i medium data. For the serum medium data, we kept 52 cells and 9440 genes. smFISH data were normalized by scaling factors which were calculated as cell sizes divided by mean of cell sizes. Total number of input spike-ins was estimated by adapting the code provided in the supplementary information of  [10]. We divided the total number of observed spike-ins in each cell by the total number of input spike-ins to obtain scaling factors.
We used smFISH data to estimate β for single cells under 2i and serum medium respectively (0. 1212 and 0. 1187 respectively, Supplementary Figure  S11a-b ). To obtain β, we normalized scaling factors (see Methods) to the corresponding β within each one of the two datasets. Data was used in the Figure  2a ,c,e,g and Supplementary figures: S11a-b.
Klein study (UMI). Single-cell RNA-seq expression data were downloaded from GEO GSE65525  [11]. ES cells data at day 0 (933 cells with 24175 genes) were used for simulations. We did not filter out any genes or cells. For estimating β, trimmed mean of each cell at 1% was used and was normalized to β set to 0. 06  [11]  (see Methods). Data was used in the Figure  1b -e, Figure  3a -b, Supplementary figures: S2 and S8a-b.
Torre study (UMI). Single-cell RNA-seq expression data were downloaded from GEO GSE99330 (GSE99330 dropseqUPM. txt. gz) [12]. This data matrix was converted to counts using a code kindly provided by the author of SAVER  [13]. There are 32287 genes and 8640 cells in the raw data. First cells with less than 2000 genes detected or where the gene "GAPDH" could not be detected were filtered out. Second, genes with mean expression less or equal to 0. 01 were removed. Third, the gene "GAPDH" was removed because it is used as a proxy for cell size  [13]  by normalizing it to the mean β which was estimated using smFISH data (see Methods). The final filtered dataset contained 9289 genes and 1134 cells. For smFISH data, we filtered out cells with "GAPDH" counts below the bottom 10 th percentile or above the top 10 th percentile of 'GAPDH" counts.
smFISH data were then normalized by the expression of GAPDH divided by GAPDH mean expression  [13]. We fitted a linear regression of the mean expression of filtered dataset on that of the normalized smFISH datset (Supplementary Figure  S11c ). The coefficient of explanatory variable was then used as β. "GAPDH" expression, which was filtered out previously, was divided by the median and multiplied by β (see Methods). Data was used in the Figure  2b ,d,f,h, Figure  3a -b, Supplementary figures: S6, S8e-f and S10a.
Soumillon study (UMI). The dataset was downloaded from GEO GSE53638 (GSE53638 D3 UMI. dat. gz for the single cell data and GSE53638 D3 Bulk UMI. dat. gz for the bulk data)  [14]. DE detection was performed between the stage-3 differentiated cells at day 0 (D3T0) and day 7 (D3T7) (23895 genes and 1949 cells)  [3]. Cells with library sizes below the bottom and above the top 5 percentiles were filtered out. Genes with mean expression across two groups greater than 0. 05 were retained, resulting in a dataset of 1754 cells with 8586 genes (832 cells and 922 cells belonging to day 0 and day 7 time-points respectively). Using the same 8586 genes in the bulk dataset, the reference DE genes were defined to be the top 1000 genes which have the greatest log fold-change in the corresponding bulk RNA-seq data  [3].
Acknowledgements We are grateful to  Dan Hebenstreit  for critical reading of the manuscript. The benchmark lists used in the Islam and Tung studies were kindly provided by  Maria K. Jaakkola  and Chengzhong Ye respectively. We would like to thank  Rhonda Bacher  for providing R code for running MAST and producing figures 3a and 3b. We would like to thank  Mo Huang  for the code for preprocessing data from the  Torre Study. We would also like to thank  Lennart Kester  for providing smFISH data used in the Grün study. This research was supported by the  UK Medical Research Council , and a  Leverhulme Research Project Grant  ( RPG-2014-408 ). WT is.
A comparative analysis regarding the type of renewable energy used by each country is also given in this section. In the studies for Denmark, Macedonia and SEE an analysis of 50% and 100% renewable energy system by the year 2030 and 2050 was investigated. EnergyPLAN and H2RES as energy planning software for optimization of microgrid components sizing were the most-used tools for performing this kind of research. The electricity demand of SEE, Macedonia, Portugal, Ireland, and Croatia is mainly supplied by thermal power plants. This form of electricity generation has covered around 60% of the total generation capacity in SEE in 2008. On the other hand, in Australia, electrical energy is mainly generated by hydropower plants. Furthermore, countries like Portugal, SEE, Ireland, and Australia due to the great hydropower potential are planning to invest mostly in this type of technology in the future.
Considering that 97% of electricity generation in Kosovo comes from coal power plants, wind, hydro and solar power does not play such a significant role in the baseline scenario 2015. In this study, an interest rate of 3% was used as it is preferred when EnergyPLAN analyses are being performed  [66]. The estimated weekly power production from all power plants together with the electricity imports and exports values for 2015 are shown in Figure  4. One can clearly see that the majority of power production was coming from fossil fuels followed by a very low percentage of hydro, solar and wind sources. A comparison of the monthly average demand value between the data extracted from KOSTT with the results obtained from modeling our system in the EnergyPLAN software is shown in Table  4. Very small differences in electricity demand data during some months of 2015 indeed represent the accuracy of the model developed in EnergyPLAN.
The estimated weekly power production from all power plants together with the electricity imports and exports values for 2015 are shown in Figure  4. One can clearly see that the majority of power production was coming from fossil fuels followed by a very low percentage of hydro, solar and wind sources. A comparison of the monthly average demand value between the data extracted from KOSTT with the results obtained from modeling our system in the EnergyPLAN software is shown in Table  4. Very small differences in electricity demand data during some months of 2015 indeed represent the accuracy of the model developed in EnergyPLAN. electricity imports and exports values for 2015 are shown in Figure  4. One can clearly see that the majority of power production was coming from fossil fuels followed by a very low percentage of hydro, solar and wind sources.
Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery.
During the 6th and 7th NBDC/DBCLS BioHackathons in 2013 and 2014, which were hosted by the National Bioscience Database Center (NBDC) and the Database Center for Life Science (DBCLS) in Japan, we focused on the improvement of Resource Description Framework (RDF) data for practical use in biomedical applications by developing guidelines, ontologies and tools especially for the genome, proteome, interactome and chemical domains. Also, to host these data effectively, we explored best practices for representing dataset metadata, as well as assessing the capabilities of triple stores and the quality of service of endpoints. The BioHackathon 2013 was held in Tokyo and BioHackathon 2014 was held in Miyagi. Both were sponsored by the NBDC and the DBCLS in the series of NBDC/DBCLS BioHackathons  [1] [2] [3] [4]  , which bring together database providers and bioinformatics software developers to make their resources integrable in effective ways.
Genomic information Genome data is a key component in modern life sciences as it serves as a hub for data integration. In the previous Bio-Hackathons, we have developed ontologies, such as the Feature Annotation Location Description Ontology (FALDO)  10  and the Genomic Feature and Variation Ontology (GFVO)  11  , and produced RDF data from heterogeneous datasets for integrated databases and applications. In this section, we describe how we modeled genomic annotations and related resources in RDF and ontologies.
Ontology for locations on biological sequences During the BioHackathon 2012  4  , it was recognized that a common schema ontology was desirable for the Semantic Web integration of sequence annotation across multiple databases. In depth group discussions including bioinformatics software developers and major database representatives identified common core needs in defining locations on biological sequences (both nucleic acids and proteins). This produced a draft specification for the Feature Annotation Location Description Ontology (FALDO), and proof of principle data conversion tools. This work continued at the BioHackathon 2013, with a specific focus on ensuring that all the existing annotations in the International Nucleotide Sequence Database Collaboration (INSDC)  12  feature tables could be converted into RDF triples using FALDO, as well as standardizing the coordinate system, and making sure that the starts of features are biologically sensible i.
e. the start value is numerically higher than the end for genes located on the reverse strand. Subsequently, in May 2014, DBCLS organized a closed meeting, the RDF Summit, where a small group of developers from DBCLS, DNA Data Bank of Japan (DDBJ), Swiss Institute of Bioinformatics (SIB), European Bioinformatics Institute (EBI) and Stanford gathered to standardize the RDF representation of genomic annotations. The group agreed to use the FALDO ontology (see the section below) for annotating the coordinates of genomic annotations and to represent gene/transcript/exons in RDF. As a result, the RDF model of DDBJ, Ensembl  13  and TogoGenome  9  are now aligned such that common SPARQL queries can retrieve sequence annotations from these distinct data sources interoperably.
Human genome and variation After defining a common RDF model to represent the INSDC feature tables, one of the major remaining needs was to standardize the RDF representation of genome variations, which was discussed during the BioHackathon 2014. A group from EBI, DBCLS and Tohoku University surveyed existing databases that represent clinical annotation of variants. National Center for Biotechnology Information (NCBI) ClinVar  14  provides information on the relationships between human genetic variation and phenotypes along with supporting evidence; Online Mendelian Inheritance in Man (OMIM)  15  provides relationships between genes and disease; Leiden Open Variant Database (LOVD)  16  provides gene variants related to colon cancer; Human Gene Mutation Database (HGMD)  17  is commercial but widely used; Thomson Reuters Gene Variant Database (GVDB) is also a commercial database.
Tohoku Medical Megabank had a license to jointly develop the RDF version of the GVDB with Thomson Reuters and they completed the initial version to test queries like "find shared variations among diseases" and "find related variations from a specific disease". In parallel, the EBI group started to convert Ensembl variation data into RDF in which an "allele" is related to "gene_variant", "sequence_alteration" and "regulatory_region_variant" instances in the sequence ontology (SO), and its location is represented by means of a FALDO region [Figure  1 ]. The H-invitational database (H-InvDB)  18, 19  group developed RDF data and an ontology for their database covering ncRNA annotations. During the Biohackathon 2013, the RDF version of the H-InvDB was expanded and its ontology was published including recent advancement in understanding of non-coding RNA (ncRNA) function.
To improve descriptions of the functional relationships between coding transcripts and ncRNA, links between transcripts in H-InvDB and two major RNA databases, Rfam 20 and miRBase 21 , were added. For miRBase, interactions between miRNA and transcripts were predicted using TargetScan  22. For both of these databases, new classes were defined in the ontology to describe interaction events, such as binding between a transcript and a miRNA. At the BioHackathon 2014, the group tried to incorporate variant information into the RDF data.
Universal Biological Sequence ID (UBSID). An essential step in the merging of datasets is relating primary identifiers i. e. any data can be joined if they contain the same identifiers. Therefore, all databases can be joined as fully connected Linked Data if appropriate universal identifiers are consistently used. To date, molecular biology has mainly developed around the Central Dogma concept in which higher levels of annotation (transcripts, proteins) are related to the underlying genomic sequence. Genes, as well as protein binding motifs and other features such as SNPs, can be related to DNA sequences, as can the transcriptome and proteome. Therefore, much of modern molecular biology data can in principle be related if the underlying nucleotide sequences are used as the basis for identifiers.
Therefore, the G-language group proposed a Universal Biological Sequence ID (UBSID) to enable this encoding. For example, the human APOE mRNA sequence is encoded as <http://rest. g-language. org/ubsid/ubsid-2seq/hg19-chr19:045409882+A42:=43-1092=193-580=718:> as a URI in the G-language REST service. Identifiers used in the Ensembl RDF. Ensembl generates their own IDs for genes, transcripts and exons in their database. For example, the human APOE gene is given an ID of ENSG00000130203, which encodes five transcripts (one of them is ENST00000252486) and one of the exons of this transcript is ENSE00003577086. It is natural to use these IDs when constructing URIs for the RDF dataset. In the 2014 development version of the Ensembl RDF, the human APOE gene is indicated as <http://rdf. ebi. ac. uk/resource/ensembl/ENSG00000130203> within a graph identified as <http://rdf.
ebi. ac. uk/dataset/ensembl/77/9606> for the human genome dataset in the Ensembl release 77. The location of this gene on human chromosome 19 is designated by <http://rdf. ebi. ac. uk/resource/ ensembl/77/chromosome:GRCh38:19:44905754-44909393:1>. The strategy to generate unique URIs for each annotation in Ensembl is different from that employed by DDBJ/INSDC and TogoGenome, which all share both the same RDF model and use the FALDO ontology to describe the actual coordinates of annotations (e. g. genes and exons) on a chromosome. Thus at present further work is needed before all these providers are completely consistent and interchangeable.