dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the Farmerline initiative in Ghana (Case Study 3) presents an effective model of data cooperation that amalgamates data from various sources. It demonstrates how data cooperatives can enhance sustainable development and food security in rural regions. However, scaling and replication of this model require supportive policies and a robust digital governance structure, underlining the necessity of political will and agency in the growth of data cooperatives. A striking example of the transformative potential of data cooperatives can be found in SOLshare (Case Study 4). This decentralized energy trading platform has brought affordable, clean energy to rural communities in Bangladesh. It emphasizes that with the right framework supporting data sharing and collaboration, data cooperatives can significantly contribute to sustainable development.
Zenzeleni (Case Study 7), a community-owned telecommunications network in South Africa, serves as an excellent example of how local ownership and collaboration can help bridge the digital divide. It also highlights the challenges such initiatives face, such as regu- The Bavarian Construction Data Cooperative, GemeinWerk (Case Study 8), presents a unique model of a sector-specific data cooperative. Facilitating trust-based data sharing among stakeholders creates a pre-competitive space of trust that drives productivity and innovation among SMEs. Lastly, Barcelona's smart city initiatives (Case Study 9) demonstrate the potential of data cooperatives in urban environments. The city has successfully leveraged data from various sources to improve urban services and enhance residents' quality of life.
Research methodology The conceptual model and the related hypotheses were tested deploying partial least square (PLS) -structural equation modelling (SEM) technique with PLS3. 2. 3 software  (Sarstedt et al. , 2017). PLS-SEM has been preferred because the approach is deemed to be flexible and can analyze a complex model in a simple way  (Lowry and Gaskin, 2014). Besides, the PLS-SEM technique has been chosen since it does not impose any sample restriction  (Willaby et al. , 2015)  and does not require the data to be normally distributed, which is the essential condition for analysis of data by covariance-based (CB) structural equation modelling (SEM) technique  (Rigdon et al. , 2017).
Moreover, to simulate domestic MGs, the SPSVI operation for tracking active power values is performed by ramp rate references based on standard IEEE Std 1547-2018 for inverters connected to the grid. The SMC-2 was appropriately tuned by a PSO algorithm through MATLAB TM and the system simulation through PSCAD TM. The results show that the algorithm performed better compared to a classical algorithm such as proportional-integral control in terms of integral absolute error (IAE) and integral square error (ISE).
C. Tuning the controller The following pseudocode 1 shows the criterion for tuning SMC-2. The search objectives are the optimal values of λ and W. Algorithm 1 Pseudocode for PSO/SMC-2 tuning. Input: N-particle population; Output: Optimal output value Initialization: position and velocity of each particle for λ and W ; LOOP Process 1: for Fitness value calculation to minimize error tracking (J); do 2: Update personal and global best (X (k) and X best(k) ); 3: end for Meeting stopping criterion; 4: return Optimal output value; In this approach, the PSO demands considerable computational cost, but it is stable. The SPVSI control with PSO tuning was modeled in MATLAB TM. The simulation of the scenarios was performed in PSCAD TM with the tuning values received by MATLAB TM , as shown in Fig. 4.
A. Active power transfer between the SPVSI and the grid To verify the performance of the aforementioned SPVSI connected to the grid, a computer-based simulation using PSCAD TM together with MATLAB TM is carried out. For validation purposes, the PSVSI scheme is used with the parameters from Table  II. The active power transfer between the SPVSI and the grid must comply with the regulations in  [22] ,  [23]. The active power transfer between the grid and the SPVSI is determined by ramp reference values to soften the energy transfer in MGs. The value of the ramp is determined to be 20% per minute of the maximum active power available. To determine the magnitude of the active power ramp, the following procedure is performed: the battery bank voltage and capacity are V bat = 400V and C bat = 100Ah, respectively.
SMC-2 dealt with the coupled model of the SPVSI in the DQ reference frame. The robustness of SMC-2 surpasses the model uncertainties presented by parasite capacitance on the SPVSI. The PSO method is suitable for finding the controller's parameters. The procedure followed in this work separately developed the greater cost computational algorithm in MATLAB TM to give the control law's parameters to the main simulation in PSCAD TM.
Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets.
Based on these outcomes, the decision was made to reduce the complexity by generating four artificial samples on only one well-known virus, pepino mosaic virus (PepMV). PepMV is a positive sense ssRNA classified in the species Pepino mosaic virus (genus Potexvirus, family Alphaflexiviridae) known to infect tomato, eggplant and potato plants. Because of the specificity of this study, we created our own artificial samples keeping in mind the good practices shown in the community effort on samples production for bioinformatic validation  (Tamisier et al. , 2021)  and the usual frequency of PepMV sequences in dataset from infected tomato. In addition, the complexity of pipelines was managed by dividing the bioinformatics pipelines for SNPs detection into three fundamental steps. The three steps were decided based on our hypothesis regarding their relative importance on the final results: read quality trimming, read assembly and SNPs calling.
In addition, the expert validation of SNPs detected was also independently analyzed. We describe in this study a performance testing of bioinformatics protocols by the end users, meaning that all the steps from the reception of HTS raw data until the interpretation of SNPs predicted were evaluated. End-users corresponded to 14 plant virology laboratories with different levels of expertise in bioinformatic and variant analyses. The study reported here evaluated the sensitivity and reproducibility of variant analysis applying various bioinformatic strategies currently used in the plant virology community. Each participating laboratory received the same datasets at the same time but without information on its composition in isolates of PePMV (blind test). To precisely decipher the impact of the three above-mentioned steps, data were sent in different formats in consecutive steps: raw sequencing reads, quality-controlled reads and aligned reads where the next format was only sent after analysis of the previous was completed.
Origin of the sequencing data Simulated samples from PepMV were generated using the sequencing reads simulator ART  (Huang et al. , 2012). ART (V2. 5. 8) was used for simulating paired-end reads using the built-in quality profile for HiSeq 2500. The reads were artificially generated from the chosen genome sequences, with mutations added manually to the different samples using multiple criteria (frequencies, length, quality). The detailed samples composition is shown in Table  1 , in addition, all datasets are freely available (https://doi. org/10. 5281/zenodo. 7431632). Sample 1 was constructed to estimate the limit of detection of low-frequency SNPs. This sample was generated by adding one bp substitution (sometimes called single nucleotide variants (SNVs) by informatic tools), one bp deletion and one bp insertion in the sequence of PepMV, isolate CH2 with reference DQ000985.
Organization of the performance testing In total, 14 laboratories from eight different countries participated in the performance testing reported here with the coordination of Liège University (Belgium). Each participant was free to choose the algorithms and parameters for each of the three defined steps of the bioinformatics analysis. Importantly, the participants carried out the process step by step, as described in Fig. 1. In order to prevent too much diversity in the approaches used, a selection of intermediate process results was used as common datasets from which each lab had to start the next step. The overall idea was to allow the comparison of method/laboratory performance as each participant used the same input files for each step. The information on the bioinformatics analyses (anonymized by replacing laboratories name by letters) carried out for all steps by the 14 participating laboratories is available in File S2.
I. Pre-processing step The participants received the three designed datasets in blind. They might, in this step, perform actions to select reads to keep for further analysis, like pairing, merging, trimming, or nothing at all. The results they were asked to send corresponded to a FASTQ file for each dataset containing sequences considered ready by the participant for the following steps. In addition, the bioinformatics analysis carried out had to be reported (algorithm and parameters used). To compare the received FASTQ files (14 files from each sample = 42 files), the PT coordinator used several criteria (see next chapter). From each original dataset, three files (referred as B, C, and D) showing the most divergent conditions of pre-processing reads (relax cleaning, average cleaning and strict cleaning) were selected for the next step.
II. Identification The nine FASTQ files (three per sample) were sent to all participants in blind (they just knew the original dataset origin). In addition, the participants carried out the analysis on their own FASTQ files generated during step 1. The goal for the participants was to identify the appropriate PepMV reference sequence(s) and to align the sequencing reads on it (them) (without the need to search for other viruses). Many different methods could be applied: de novo assembly, mapping, Kmer search and dendrogram. The expected result was the mapping of reads on the correct reference(s) in bam format. The success of this step depended on the participant's ability to identify the correct reference(s). Further on, the coordinator analyzed the 140 bam (alignment) files received (14 laboratories × (3 samples × 3 datasets + own file)) using several criteria: number of reads mapped, the alignment quality, and coverage.
III. Variant calling The nine bam files were sent in blind to the participants (they only knew the original dataset). In addition, each participant also processed the bam file generated from their own analysis (if any). This step involved identifying the SNPs present in each bam file by any method of their choice and confirming the detection (manual confirmation). Criteria for data analysis at each step For the pre-processing step, the result comparison was based on classical reads statistics: number, quality and length. In addition, the tools and parameters used by the participants (pairing, merging, adapter removing, quality trimming, duplicate trimming) were gathered. The procedure can change the expected output significantly if duplicates are removed and reads are paired and merged, generating a lower number but sometimes longer (when merged) reads.
Result impacting decision on protocol Step 1: reads pre-processing The objective of the pre-processing step was to evaluate the difference in the read-cleaning strategies of participants and to further evaluate their impact on the next steps of the analysis. We obtained several FASTQ files from each participant (65 files in total); the statistics on the reads retained after this step files were studied and are available in File S3. The data transformations carried out by the participants, in various combinations were: Pairing: associate the two reads of the same pair together; the modification made on one of the reads may impact the second one. Merging: merge the two paired reads to obtain one longer read. Adapter trimming: removing small (Illumina) adapters that may be on reads because they can interfere with downstream analyses.
Quality trimming: remove nucleotides (or read) based on their base calling quality score. De-duplication: remove identical reads if several are present. Not all of these data transformations were performed by all laboratories, and the order in which they were performed was also different. These transformations and their order are important because they can impact the number of reads, their average length and the quality of the remaining reads, which were the metric for the dataset selection for the following step. The individual effect of each transformation on reads is known  (Lebas et al. , 2022). In our case, it is their combination that is interesting. The results were analyzed according to two axes. First, the consistency in read number, quality, and length between the original file and the cleaned ones were checked between the three samples.
In the second axis, the differences observed in applied methodology and obtained results were compared between laboratories. An obvious trend is that a higher average base quality is obtained when more nucleotides are removed. Merging was performed by three participants and was identified as a critical step in comparing the different datasets since it significantly changed the statistics. Therefore, those datasets were analyzed separately from the non-merged ones. When merging, the quality of the remaining read is higher, but the number of remaining nucleotides is lower. That is expected since the overlapping part of a pair of reads is used for merging. On the non-merged resulting FASTQ files, we saw that similar combination of methods provided similar results, with the slight difference explained by the variation of parameter/version of used tools.
No unexpected results were obtained on these steps when analyzed alone (before comparing to other step). To monitor the eventual effect of the pre-processing on the overall analysis, a selection of datasets to be used by all labs for the next step was performed. The criteria for the selection were discussed and agreed among participants. A combination of metrics corresponding to read number and length (impactful on coverage ability which is believed to be very significant for later variant calling), and quality score was used to select the datasets that are described in Table  2. The Illumina adapter effect on the trimming was not evaluated as they are not generated by the read simulator tool. Condition B was kept for step 2 to include one file with merged reads as its result files were more constant in terms of high quality and read number (operations used to generate each dataset are available in File S2).
It also presented a higher number of merged reads than the remaining unmerged reads (File S3). This dataset represented high-quality reads mostly merged. Conditions C had better quality confidence than average, even if it provided fewer reads than the others, which may play a key role in the last step. Condition D is similar to the majority of other datasets by most metrics. The fact that it did not have any outlier metrics made it a good candidate for the next step as a standard read cleaning case representative. Step 2: Reference identification and read mapping The participants aimed to identify the correct reference(s) and to provide a mapping file for further variant calling. Information about those results was extracted and detailed in File S4. The key result of this step was reference identification.
The participants used a variation of assemblers (Geneious, CLC Genomics Workbench, Tadpole, Velvet, Spades) and/or alignment (BLAST or mapping) approaches. Alternative strategies of de novo assembly and mapping to all PepMV references (and no other virus species) were used by one participant, while de novo assembly and BLAST to a database containing all PepMV references (and no other virus species) were used by two participants. Most (8) laboratories used an approach that works without the virus prior knowledge with (de novo) assembly and alignment (BLAST) to a database of various (plant) viruses. No major difference between strategies or tools was found. In total, of the 14 participants, 11 found the correct (DQ000985. 1) reference for sample 1, and 10 labs found the two correct references (DQ000985.
1, AJ606359. 1) for sample 2 (two labs found reference DQ000985. 1 only). Finally, 11 labs found the correct reference (DQ000985. 1) for sample 3. We investigated the methods used to find the reference, focusing on the case(s) where it failed and identifying four different causes for not identifying the correct reference(s): The alignment (BLAST) database used for reference identification was too small (the correct references were not present). The database (BLAST) contained redundancies, meaning several identical reference IDs were found. This will not impact variant calling ability. Nevertheless, if several identical sequences are used for the mapping, the reads are likely to be divided between references depending on the mapping options, impacting the respecting SNPs frequencies and the confidence of their detections.
Several PepMV references are described, while only one was correct (incomplete BLAST selection criteria were applied) Only the best BLAST result was kept, meaning that the second reference for sample 2 was not identified. Table  2  summary of quality metrics of the nine selected datasets for the following steps. The metrics number of reads (Read NB), read length range (minimum to maximum read length), mean (read) length and the number of nucleotides (NB nucleotides) were used to monitor the remaining amount of informative data remaining. Mean confidence (based on confidence scores provided by the base calling program) and the quality score metrics were used to assess the quality/confidence of the remaining data. Quality scores are an estimation of the probability of a base (call) being wrong, Q20 represent 1 in 100 error rates, Q30 is 1 in 1,000, Q40 1 in 10,000, meaning that a higher Q score indicates a smaller probability of error, but keeping only the high quality reads mean less informative data kept for further analysis.
In all conditions, the number of identical sites dropped below 1%, probably because sample 3 presented lower-quality reads than the two other samples. Because the difference between statistics was not so high, we decided to select the new conditions (E, F, G) for step 3 based on the tool/parameters used. Condition E represented the mapping results obtained using CLC genomics (QIAGEN CLC Genomics Workbench) with standard parameters (see lab B mapping parameters in File S2) used; the number of reads in that file was close to the expectation (see Table  1 ). Condition F is a case with Geneious standard parameters (see lab D mapping parameters in File S2) applied and read numbers also close to expectation. Finally, Condition G used Geneious with a more relaxed parameter, using the same parameter set as dataset F but allowing more mismatches (see lab Z mapping parameters in File S2) and presented divergence on identical site numbers.
The reasoning behind this selection was to test if the same SNPs would be predicted between the tools/parameters (meaning that the same reads were mapped at the same position). For example, the comparison between F and G should provide interesting information since the reads number was very close; the differences seen (if any) would be due to the very few read differences. Step 3: SNPs calling We obtained 9,624 SNPs detections, which were analyzed by comparing them to the expected SNPs using three parameters (mutation type, position of the SNPs, and reference/ allele). All laboratories gave results using the forward strand coordinate, meaning that the reads were automatically reversed by the tool(s) used for the mapping. Table  4  shows the expected SNPs' results compared to the predicted ones grouped by dataset conditions combined.
For example, dataset BE represents the merged reads (with high quality) mapped with CLC Genomics with standard parameters. By knowing the true positive percentages (TP%) and true positive rate, we can deduce the false positive percentages (FP%) and False negative rate (expected SNPs missed). There are no major differences between alternative pre-processing datasets (B, C, D), with 75% TP on average for all three conditions. On the alternative mapping parameter, the percentage of true positives decreases from dataset E (90% TP) to F (76% TP) and G (59% TP). On G (relaxed parameters for mapping), an overprediction of SNPs (last column) was observed and explains the lower percentage. Contrary to what was expected, sample 3, which was designed to be the most complicated sample, showed the best results (86% TP for all conditions).
Importance of reference identification The second step, including the identification of the reference genome to be used for mapping, was very important as, if it failed, the variant calling would be strongly biased. We identified the database composition as the main reason for the possible failure. Indeed, the scope of the reference database is important because if not enough representative sequences are present, the compared sequences might be too distant, and overprediction of SNPs can occur. In fact, the more reference sequences in the database, the better it is for the detection, but the analysis will take longer. In general, using the most closely related reference as possible is more beneficial, but if another (little bit less related) reference is used, this will also allow to reconstruct the genome, and investigate SNPs.
Most participants used a de novo assembly to obtain contigs (sequence fragments easier to identify). They then performed a first BLAST against a RefSeq database (including only one representative sequence) to have a first (quick) overview of the putative identity of the contigs. Then, as the contigs linked to PepMV were identified, they used the contigs (all of them or the pre-selected PepMV one) as input for a second BLAST analysis against a larger database containing all PepMV sequences to select the closest reference. One of the laboratories used an alternative approach that also worked; first, the virus identification was performed using a similar approach (assembly and alignment) through the VirusDetect pipeline  (Zheng et al. , 2017). Once the PepMV virus was confirmed, all reference sequences for that virus were downloaded, and whole genome alignments with a phylogenetic tree construction were performed.