dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual off-target datasets and modification frequency We obtained lists of guide sequences and their off-targets from studies  [3, 7, [26] [27] [28] [29]  that tested 20-bp-long guide sequences. Data were extracted from supplemental files with the PDF table extraction software Tabula (http:// tabula. technology/). In the case of a study that tested both 19-and 20-bp guides  [33] , for consistency we used only the 20-bp guide data but included data from both cell lines (HAP1 and K562). For all studies, we obtained a measure of cleavage, the "modification frequency", the number of all successful genome insertions or deletions divided by all observations at the respective off-target site, as reported by  [7]  and  [29]. For a study that quantified modifications using both sequencing and lentiviral insertions  [38] , we did not use the low-resolution number of lentiviral insertion sites but rather the frequencies from targeted sequencing, which the authors kindly shared with us.
Tsai et al. [3]  measured only successful modification events, so as an approximation of modification frequency we divided reads per site by all reads obtained for one guide. Two studies  [3, 28]  observe only modifications, so the sum of the frequencies of a single guide is always 1. 0, which is not the case for the other datasets. Frock et al. [28]  did not directly quantify genomic insertions or deletions but counted the correlated events "lentiviral insertion", which samples relatively rare cleavage events and may as a result overestimate real modification frequencies. The complete dataset consists of 30 guide sequences tested by 36 assays, 634 off-target sequences, and 697 cleavage frequencies, as some off-targets were detected by different assays. For an overview of all off-target studies see Additional file 1: Table  S1 ; for the complete offtarget dataset see Additional file 2.
Off-target scores From the description in the article  [7]  there are several possibilities to calculate the Hsu score; we used only the normalized aggregate frequencies which also gave the highest AUC. The off-target and specificity score of the MIT website were implemented based on source code by Hari Jay (https://snipt. net/harijay/fz-score-d1324dab/). We implemented the CROP-IT and CCTop off-target scores from the description in the original articles  [36, 37]. For the CFD score  [34] , we received source code from the authors.
Previously published knock-out and cleavage efficiency datasets The efficiency studies are summarized in Additional file 10: Table  S5. We used the human knock-out efficiency dataset from Wang et al. as provided by Xu et al. [23, 38]  for HL60 cells, inversing the sign, such that higher values mean a more efficient knock-out, as in the other studies. The dataset by Doench et al. [8]  was used as rank-percent values as provided; we also converted parts of the raw data to log-abundance values, as described in their study, and split them by exon and cell type. For the newer dataset from Doench et al. [34] , we used their Supplemental Table  16  and only the eight genes with reproducible results across the treatments (CCDC101, CUL3, HPRT1, MED12, NF1, NF2, TADA1, TADA2B) as recommended in their study.
Guides that did not uniquely map to the human genome (hg19) were removed, resulting in 2333 guides. For Chari et al. [24] , we used only the Streptococcus pyogenes dataset from 293 T cells, as the K562 dataset was not correlated with any score nor their 293 T results. Datasets from  [6, 25, 31, 40, 42, 43]  were used as provided. At first we did not obtain any significant correlation for the dataset by Housden et al. [44]  and after notifying the authors and received a corrected version of their Additional file 1: Table  S1. For the dataset by Hart et al. [41] , we received the log-fold changes in five different cell lines and time points 8-18 days from the authors. The guides in this study were selected to have a GC content in the range 45-70 % and no T in the last 4 bp. We kept only data for 4293 guides against the 829 genes determined to be essential by the authors in all five cell lines, used the average over all time points as the assay result, and used only the result from the Hct116, library 1, replicate 1 which was the only cell line with a replicate and with a high correlation between both replicates and the focus of the original study (Additional file 10: Table  S5 ).
A very recent CRISPR library  [47]  was designed using the Wang score and, due to this bias, is not usable for our evaluation. All sequences without genomic coordinates were mapped with BLAT  [48]  to the respective genome and extended by 50 bp on both sides of the protospacer adjacent motif (PAM) to provide enough flanking sequences for the score calculations. For the Doench 2014 dataset, duplicate genomic hits were resolved manually to a single hit. For the other datasets, guides with duplicate matches were skipped. All tested guide sequences and their reported efficiencies are available in Additional file 12.
New cleavage efficiency datasets Our first cell culture dataset ("Schönig" in Additional file 14 and Additional file 10: Table  S5 ) is a set of 24 guide sequences, three guides each from eight loci, one locus in human, five in rats, two in mice, tested with a lacZ nuclease activity assay  [46]. For this purpose, the guide RNA target regions were inserted into a nuclease reporter plasmid (pTAL-Rep 37 ) in between a partly duplicated, nonfunctional β-galactosidase gene. Upon transfection of the reporter plasmid and px330-U6based guide RNA expression vectors  [49]  into HeLa cells, nuclease-induced double-strand breaks stimulate the repair of the gene segments into a functional reporter gene, the activity of which is determined in cell lysates using an o-nitrophenyl-β-D-galactopyranosid (ONPG) assay.
A luciferase expression vector was also added to the transfection mix and luciferase activity was measured as transfection control. Each sample activity was ranked from 1 to 3, with 3 representing strongest cleavage. The P value in the text is the probability to obtain six or more guides with an activity of 3 when randomly drawing one guide per locus and repeating the sampling 100,000 times. The second cell culture dataset ("Concordet" in Additional file 14 and Additional file 10: Table  S5 ) was obtained from 52 guide sequences targeting 14 loci. The guides were cloned into the MLM3636 plasmid with a U6 promoter (Addgene #43860, from the KJ Joung lab), and electroporated into cells. PCR products were tested with the T7 endonuclease assay  [50]  and mutated sequences quantified by gel electrophoresis.
The result of the T7 assay was reported as the numbers 1, 2, or 3 based on the mutation rate: 1, inactive guides; 2, not very active guides; 3, efficient guides (mutation rate > 10 %). In total, 26 guides from the human genome were tested in U2OS cells, 18 mouse guides in mouse embryonic fibroblast (MEF) cells, and eight rat guides in C6 cells. Our first zebrafish dataset ("Eschstruth" in Additional file 14) is a set of 18 guide sequences targeting a single locus in zebrafish. Three of the guides were selected because of their high Doench scores. We injected 20-50 pg gRNA transcribed from a T7 promoter into zebrafish onecell embryos with 300 pg Cas9 mRNA. Cleavage efficiency was measured on 16 single embryos 24 h post-fertilization with the T7 assay, a standard protocol described previously  [50]  and classified into three categories: no cleavage (1), low cleavage (2), and high cleavage (3).
This dataset is shown in Additional file 16: Figure  S6 , but not Fig. 5  as it is small and the extremely high Doench scores (top 3 %) were used to select the guide sequences, so it is biased compared with the other datasets from this study, where guides were selected without using any predictions. Our second zebrafish dataset ("Shkumatava" in Additional file 14 and Additional file 10: Table  S5 ) is a set of 103 guide sequences from 11 different loci in zebrafish. Guides were transcribed in vitro with the T7 RNA polymerase kit. No guide was selected based on efficiency scores. Guide RNA (10 pg) and 150-200 pg of Cas9 mRNA were injected into wild-type AB zebrafish at the one-cell stage. Cleavage efficiency was measured by extracting genomic DNA from around 20 embryos, PCR of the target regions, cloning the result into a TOPO-vector, and shipping for Sanger sequencing a number of colonies in the range 10-20.
The result of the assay is the number of sequences with mutations over all sequences. Guides were manually assigned to a locus if they were located closer together than 3 kbp. The P value in the text is the probability to obtain at least nine successes, where a success is defined as finding at least one guide with a modification frequency among the top two values in a locus when selecting two guides randomly from each locus. The sampling was repeated 100,000 times. For our mouse in vivo dataset of 30 guides ("Teboul in vivo" in Additional file 14), single guide RNAs (sgRNAs) were synthesized using a MEGAshortscript T7 Transcription kit (Ambion). RNAs were purified using a MEGAclear kit (Ambion). RNA quality was assessed using a NanoDrop (Thermo Scientific) and by electrophoresis on 2 % agarose gel containing ethidium bromide (Fisher Scientific).
Scoring functions Some of the original articles did not include source code. We implemented the efficiency score by Doench et al. [8]  and shared it with Doench et al. , who made it available on their website. As a result, our source code has already been used in a study by Xu et al. [23]  for an evaluation of the Doench score. We also had to reimplement the Moreno-Mateos score based on the description in the article  [39] , as the authors declined to share code. R code to calculate their scoring function was gratefully provided by Wang et al. [38]. We subtracted the result from 1. 0 such that higher scores correspond to a better knock out, like all other scores. For the CRISPOR website and better performance, we had to reimplement this score. Even though we are using the same SVM library (libsvm) via scikit-learn  [52] , the results are slightly different (Pearson R = 0.
97, 85 % of the differences are <0. 1); the analysis in this article is based on the original R code. Housden et al. gratefully provided Java source code, which we translated it to Python for easier integration into our website. To obtain Fusi et al. [53]  scores, we used the web service at https://www. microsoft. com/en-us/research/ project/azimuth/ an API-key gratefully provided by the authors. For a fair comparison with the other algorithms, we did not specify the optional parameter, the position of the guide within the gene. The source code for the Wong et al. score  [45]  was obtained from the WU-CRISPR website (http://crispr. wustl. edu/) and slightly modified to allow parallel processing of input files. All efficiency score calculations have been bundled into a Python library (crisporEffScores.
py) available from the Github repository accompanying this article (see below). The module includes compiled third-party libraries and their source code: the Xu et al. score and the SVMlight and libSVM libraries that are the basis for the Chari et al. and Wang et al. scores. We hope that authors of new efficiency scores add their code to this module for easier evaluation and integration into future guide selector websites. To evaluate other off-target predictors, we wrote scripts that automated the web browser Firefox and pasted the guides individually into the CRISPR Design website (http://crispr. mit. edu) and downloaded the results, in total roughly 12,000 predicted off-targets. Tool implementation and source code availability CRISPOR uses the popular BWA aligner  [35]  version 0. 7. 5a-r405 in iterative mode ("-N").
Acknowledgements We thank  Donna Karolchik  for copy editing of the manuscript and  Jean-Baptiste Penigault  for the design of the CRISPOR site. We thank  Hari Jay  for making his implementation of the MIT guide specificity score available on the Cameron  Ross MacPherson  for his corrections to our reimplementation of the Doench et al. score,  Charles Vejnar  for sharing their efficiency dataset (Moreno-Mateos et al. ),  Xiaoxi Liu  for sharing their efficiency dataset,  Benjamin Housden  for immediately sharing their efficiency dataset and correcting their Additional file 1:  Table S1 ,  Tim Wang  for sharing their SVM score source code and efficiency dataset,  John Doench  for sharing the efficiency and off-target scoring source code,  Raj Chari  and  Xiaowei Wang  for sharing the table of raw tenfold crossvalidation data from the Chari et al.
Currents across the bilayer were recorded using Planar Lipid Bilayer Workstation (Warner Instruments). The cis compartment was connected to the head stage input and the trans compartment was held at virtual ground via a pair of matched Ag/AgCl electrodes. Signals from voltage-clamped BLM were high-pass-filtered at 2. 1 kHz using an eight-pole Bessel filter LPF-8 (Warner Instruments), digitized using Data Translation Digitizer and recorded on PC using in-house software.
Acknowledgments This work was supported by the  Wellcome/MRC Parkinson's Disease Consortium  grant to  UCL/IoN , the  University of Sheffield  and the  MRC Protein Phosphorylation Unit at the University of Dundee  (grant number  WT089698 ), the  Leverhulme Trust. SG is a  Wellcome Trust   Intermediate Clinical Fellow. The work was undertaken at  University College London Hospitals (UCLH)  and  University College London  ( UCL ), who receives support from the  Department of Health's NIHR Biomedical Research Centre 's funding streams.
We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science.
7 for a full empirical report). Unique among the replication initiatives thus far, the pipeline project corpus includes the data from not only the replications but also all of the original studies targeted for replication. This creates a unique opportunity for future analysts to better understand reproducibility and irreproducibility in science, since the data from the original studies can be reanalyzed to better understand why a particular effect did or did not prove reliable. The dataset is complemented by both socioeconomic and demographic information on the research participants, and contains data from 6 countries (the United States, Canada, the Netherlands, France, Germany, and China) and replications in 4 languages (English, French, German, and Chinese). The Pre-Publication Independent Replication Project dataset is publicly available on the Open Science Framework (Data Citation 1) and is accompanied by SPSS syntax which can be used to reproduce the analyses.
Participants The Pre-Publication Independent Replication Project corpus includes three datasets. The first dataset (PPIR 1. sav: Data Citation 1) contains data from 3 original studies and their replications, a second dataset (PPIR 2. sav: Data Citation 1) contains data from 3 original studies and their replications, and a third dataset (PPIR 3. sav: Data Citation 1) contains data from 4 original studies and their replications. In total data were collected from 11,805 participants. The first SPSS file (PPIR 1. sav: Data Citation 1) contains data from 3,944 participants (including 514 from the original studies), while the second SPSS file (PPIR 2. sav: Data Citation 1) contains data from 3,919 participants (including 351 from original studies) and the final SPSS file (PPIR 3. sav: Data Citation 1) contains data from 3,829 participants (including 582 from original studies).
Testing procedure The data were collected using both online and paper-pencil surveys from the respective laboratories and participants. The replications used the same materials and measurements as in the original studies, with the exception that the materials were translated into multiple languages. In the online version of the replications, Qualtrics was used to collect the data. This online platform allowed us to randomize the order by which the studies were presented. In order to prevent participant fatigue, studies were administered in one of three batches, each batch contained three to four studies, and study order was counterbalanced between subjects. Once subjects agreed to participate in the study, they read vignettes (see below for an example of the vignette from the Cold-Hearted Prosociality study) and completed survey questions assessing their reactions.
(PPIR 1. sav: Data Citation 1) Burn in Hell Burn-in-Hell Effect. Participants perceive corporate executives as more likely to burn in hell than members of social categories defined by antisocial behavior, such as vandals. This reflects very negative assumptions about senior business leaders. 'Vandals' is a social category defined by bad behavior; 'corporate executive' is simply an organizational role. However, the assumed behaviors of a corporate executive appear negative enough to warrant moral censure. Within Subjects 1 Participants estimated the percentage of individuals from a variety of social categories who would burn in hell (given that hell exists). The categories were: social workers, drug dealers, shoplifters, nonhandicapped people who park in the handicapped spot, top executives at big corporations, people who sell prescription pain killers to addicts, people who kick their dog when they've had a bad day, car thieves, and vandals who spray graffiti on public property.
Presumption of Guilt Presumption of Guilt Effect. For a company, failing to respond to accusations of misconduct leads to similar judgments as being investigated and found guilty. Companies accused of wrongdoing may be simply assumed to be guilty until proven otherwise. Inaction or 'no comment' responses to public accusations may be in effect an admission of guilt. Between Subjects 4 Company evaluations: Participants evaluated the company on nine-point scales along the dimensions Bad-Good, Unethical-Ethical, Immoral-Moral, Irresponsible-Responsible, Deceitful-Honest, and Guilty-Innocent. (PPIR 2. sav: Data Citation 1) The Moral Cliff Moral Cliff Effect. A company that airbrushes the model in their skin cream advertisement to make her skin look perfect is seen as more dishonest, illintentioned, and deserving of punishment than a company that hires a model whose skin already looks perfect.
Between Subjects 2 Person judgments: To assess character-based judgments, participants were asked whether Jack was a disrespectful person, had a good moral conscience, was a good person, and was the type of person they would want as a friend (1 = Not at all, 7 = Definitely; *The items moral conscience, good person and close friend should be reverse scored such that higher scores indicate more negative person judgments). (PPIR 3. sav: Data Citation 1).
Items were scored and aggregated so that lower numbers reflected viewing the medical research assistant's actions as more praiseworthy. Moral traits. Participants also assessed who was more caring, cold-hearted, aggressive, and kind-hearted (1 = definitely Karen, 7 = definitely Lisa; *reverse coded in condition Karen-items 2 and 3). Items were scored and aggregated so that lower numbers reflected more positive trait attributions regarding the medical research assistant. (PPIR 3. sav: Data Citation 1).
Data Records All data records listed in this section are available from the Open Science Framework (Data Citation 1) and can be downloaded without an OSF account. The datasets were anonymized to remove any information that could identify the participant responses, such as identification numbers from Amazon's Mechanical Turk. The analysis was conducted with SPSS version 20 and detailed SPSS syntax (including comments) are provided to help with data analysis. In total there are 3 datasets and 11 syntax files available. These datasets are also accompanied by a codebook which describes the variables, the coding transformations necessary to replicate the analyses, and a synopsis of the respective studies. Introduction to PPIR project, outline of transformations, descriptions and labels for variables from three datasets.
sav: Data Citation 1) Higher Standard Effect 11 265 participants from MTurk 2,888 participants (PPIR 3. sav: Data Citation 1) Cold Hearted Prosociality 12 79 participants from MTurk 3,016 participants (PPIR 3. sav: Data Citation 1) Bigot-Misanthrope 12 46 participants from MTurk 3,040 participants (PPIR 3. sav: Data Citation 1) Belief-Act Inconsistency 12 192 students at Northwestern University 3,708 participants.
The impact of the investigational human immunodeficiency virus type 1 (HIV-1) F4/AS01 B vaccine on HIV-1 viral load (VL) was evaluated in antiretroviral therapy (ART)-naive HIV-1 infected adults. This phase IIb, observer-blind study (NCT01218113), included ART-naive HIV-1 infected adults aged 18 to 55 years. Participants were randomized to receive 2 (F4/AS01 B _2 group, N ¼ 64) or 3 (F4/ AS01 B _3 group, N ¼ 62) doses of F4/AS01 B or placebo (control group, N ¼ 64) at weeks 0, 4, and 28. Efficacy (HIV-1 VL, CD4 þ T-cell count, ART initiation, and HIV-related clinical events), safety, and immunogenicity (antibody and T-cell responses) were evaluated during 48 weeks. At week 48, based on a mixed model, no statistically significant difference in HIV-1 VL change from baseline was demonstrated between F4/AS01 B _2 and control group (0.
Study Design and Participants This phase IIb, observer-blind, randomized study was conducted in 15 centers in the United States, 10 in France, 8 in Germany, and 7 in Spain between November 2010 and November 2012. Participants were ART-naive HIV-1 infected adults aged 18 to 55 years at the time of 1st vaccination, who were under the care of HIV physicians for !6 months (or !12 months if they initially presented with a clinical diagnosis of primary HIV-1 infection), with CD4 þ T-cell count >500 cells per mm 3 and HIV-1 VL level between 2000 and 80 000 copies/ mL at screening, and with no planned ART initiation within the next 12 months. Standard eligibility and exclusion criteria were used for enrollment, as detailed in the ClinicalTrials. gov registry (NCT01218113). Participants were randomized (1:1:1) to receive 3 doses of F4/AS01 B at weeks 0, 4, and 28 (F4/AS01 B _3 group); 2 doses of F4/AS01 B at weeks 0 and 4, and 1 dose of placebo at week 28 (F4/AS01 B _2 group); or 3 doses of placebo at weeks 0, 4, and 28 (control group).
Efficacy analyses were performed on the modified TVC that was predefined by protocol to include all eligible participants, who received at least 1 vaccine dose and with sufficient data to perform the efficacy analysis. For study participants who initiated ART treatment during the study period, CD4 þ T-cell count and HIV-1 VL data were only considered if they were collected before ART initiation and were censored after that timepoint. Moreover, CD4 þ T-cell count, HIV-1 VL, and ART initiation data were censored if they were recorded after an active study vaccine administration that was missed (not for placebo), not performed according-to-protocol (ATP), or performed after a medication or concomitant vaccination that led to discontinuation of the participant. Changes from baseline in terms of mean CD4 þ T-cell count and median HIV-1 VL were evaluated for each group and at each timepoint.