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Supplementary data tables for the dissertation "Hybridization dynamics and population genomics of a Manacus hybrid zone." This work focuses on the dynamics of hybridization over time in two species of tropical birds, the golden-collared manakin (Manacus vitellinus) and white-collared manakin (Manacus candei) comparing data from historical museum samples and contemporary wild-caught birds. Table A1 contains the sample metadata for the Manacus Restriction site-associated DNA sequencing dataset used in the dissertation with associated NCBI Biosample Accession numbers, Smithsonian Museum of Natural History number (where applicable), sample IDs, sampling site locations, and sample information of year the sample was taken, age, and sex. Table A6 contains phenotypic measurements of male plumage traits of manakins used in cline analyses to assess hybrid zone movement over time in historical and contemporary datasets, including beard length (mm), epaulet width (mm), tail length (mm), collar color (nm), and belly color (nm). Table A7 contains a summary of male plumage measurements across the hybrid zone. Table C1 contains a list of annotated protein coding genes in candidate regions of interest in Manacus genomes using outlier regions of genomic divergence, linkage disequilibrium, and enrichment of parental private alleles.

This data set includes information on mixing metric values and distances to determine the average length scale, rates and variability of mixing downstream of 43 river confluences for 150 mixing events. The file "pmx_all data.csv" contains confluence names, the number of events per confluence site, and Pmx values measured at various actual and dimensionless downstream distances. The file "pmx_binned data.csv" provides mean Pmx values within 0.5-unit dimensionless distance bins.

The dominant strategy for tailoring the chain-length distribution of free fatty acids (FFA) synthesized by heterologous hosts is expression of a selective acyl-acyl carrier protein (ACP) thioesterase. However, few of these enzymes can generate a precise (greater than 90% of a desired chain-length) product distribution when expressed in a microbial or plant host. The presence of alternative chain-lengths can complicate purification in situations where blends of fatty acids are not desired. We report the assessment of several strategies for improving the dodecanoyl-ACP thioesterase from the California bay laurel to exhibit more selective production of medium-chain free fatty acids to near exclusivity. We demonstrated that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) was an effective library screening technique for identification of thioesterase variants with favorable shifts in chain-length specificity. This strategy proved to be a more effective screening technique than several rational approaches discussed herein. With this data, we isolated four thioesterase variants which exhibited a more selective FFA distribution over wildtype when expressed in the fatty acid accumulating E. coli strain, RL08. We then combined mutations from the MALDI isolates to generate BTE-MMD19, a thioesterase variant capable of producing free fatty acids consisting of 90% of C12 products. Of the four mutations which conferred a specificity shift, we noted that three affected the shape of the binding pocket, while one occurred on the positively charged acyl carrier protein landing pad. Finally, we fused the maltose binding protein (MBP) from E. coli to the N – terminus of BTE-MMD19 to improve enzyme solubility and achieve a titer of 1.9 g per L of twelve-carbon fatty acids in a shake flask.

Wikipedia category tree embeddings based on wikipedia SQL dump dated 2017-09-20 (<a href="https://archive.org/download/enwiki-20170920">https://archive.org/download/enwiki-20170920</a>) created using the following algorithms: * Node2vec * Poincare embedding * Elmo model on the category title The following files are present: * wiki_cat_elmo.txt.gz (15G) - Elmo embeddings. Format: category_name (space replaced with "_") <tab> 300 dim space separated embedding. * wiki_cat_elmo.txt.w2v.gz (15G) - Elmo embeddings. Format: word2vec format can be loaded using Gensin Word2VecKeyedVector.load_word2vec_format. * elmo_keyedvectors.tar.gz - Gensim Word2VecKeyedVector format of Elmo embeddings. Nodes are indexed using * node2vec.tar.gz (3.4G) - Gensim word2vec model which has node2vec embedding for each category identified using the position (starting from 0) in category.txt * poincare.tar.gz (1.8G) - Gensim poincare embedding model which has poincare embedding for each category identified using the position (starting from 0) in category.txt * wiki_category_random_walks.txt.gz (1.5G) - Random walks generated by node2vec algorithm (https://github.com/aditya-grover/node2vec/tree/master/node2vec_spark), each category identified using the position (starting from 0) in category.txt * categories.txt - One category name per line (with spaces). The line number (starting from 0) is used as category ID in many other files. * category_edges.txt - Category edges based on category names (with spaces). Format from_category <tab> to_category * category_edges_ids.txt - Category edges based on category ids, each category identified using the position (starting from 1) in category.txt * wiki_cats-G.json - NetworkX format of category graph, each category identified using the position (starting from 1) in category.txt Software used: * <a href="https://github.com/napsternxg/WikiUtils">https://github.com/napsternxg/WikiUtils</a> - Processing sql dumps * <a href="https://github.com/napsternxg/node2vec">https://github.com/napsternxg/node2vec</a> - Generate random walks for node2vec * <a href="https://github.com/RaRe-Technologies/gensim">https://github.com/RaRe-Technologies/gensim</a> (version 3.4.0) - generating node2vec embeddings from random walks generated usinde node2vec algorithm * <a href="https://github.com/allenai/allennlp">https://github.com/allenai/allennlp</a> (version 0.8.2) - Generate elmo embeddings for each category title Code used: * wiki_cat_node2vec_commands.sh - Commands used to * wiki_cat_generate_elmo_embeddings.py - generate elmo embeddings * wiki_cat_poincare_embedding.py - generate poincare embeddings

Bibliotelemetry data are provided in support of the evaluation of Internet of Things (IoT) middleware within library collections. IoT infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. A modular evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of bibliographic identifiers as they appeared throughout the global module system, serving to provide context to the searching and browsing data that are the focus of this study.

The Empoascini_morph_data.nex text file contains the original data used in the phylogenetic analyses of Xu et al. (Systematic Entomology, in review). The text file is marked up according to the standard NEXUS format commonly used by various phylogenetic analysis software packages. The file will be parsed automatically by a variety of programs that recognize NEXUS as a standard bioinformatics file format. The first nine lines of the file indicate the file type (Nexus), that 110 taxa were analyzed, that a total of 99 characters were analyzed, the format of the data, and specification for symbols used in the dataset to indicate different character states. For species that have more than one state for a particular character, the states are enclosed in square brackets. Question marks represent missing data.The pdf file, Appendix1.pdf, is available here and describes the morphological characters and character states that were scored in the dataset. The data analyses are described in the cited original paper.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under grassland cover, no-tillage, cover crops, residue return and organic amendments were compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include the following key descriptor variables and covariates useful for development of scoring functions: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class, pH, and SOC, 3) depth and timing of soil sampling, 4) analytical methods for SQI quantification, 5) units used in published works (i.e. equivalent mass, concentration), 6) SQI abundances, and 7) statistical significance of difference comparisons. *Note: Blank values in tables are considered unreported data.

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

This deposit contains all raw data and analysis from the paper "In-cell titration of small solutes controls protein stability and aggregation". Data is collected into several types: 1) analysis*.tar.gz are the analysis scripts and the resulting data for each cell. The numbers correspond to the numbers shown in Fig.S1. (in publication) 2) scripts.tar.gz contains helper scripts to create the dataset in bash format. 3) input.tar.gz contains headers and other information that is fed into bash scripts to create the dataset. 4) All rawData*.tar.gz are tarballs of the data of cells in different solutes in .mat files readable by matlab, as follows: - Each experiment included in the publication is represented by two matlab files: (1) a calibration jump under amber illumination (_calib.mat suffix) (2) a full jump under blue illumination (FRET data) - Each file contains the following fields: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coordleft - coordinates of cropped and aligned acceptor channel on the original image &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coordright - coordinates of cropped and aligned donor channel on the original image] &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dataleft - a 3d 12-bit integer matrix containing acceptor channel flourescence for each pixel and time step. Not available in _calib files &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;dataright - a 3d 12-bit integer matrix containing donor channel flourescence for each pixel and time step. This will be mCherry in _calib files and AcGFP in data files. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;frame1 - original image size &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;imgstd - cropped dimensions &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;numFrames - number of frames in dataleft and dataright &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;videos - a structure file containing camera data. Specifically, videos.TimeStamp includes the time from each frame.

Miscanthus is a close relative of saccharum and a potentially valuable genetic resource for improving sugarcane. Differences in flowering time within and between miscanthus and saccharum hinders intra- and interspecific hybridizations. A series of greenhouse experiments were conducted over three years to determine how to synchronize flowering time of saccharum and miscanthus genotypes. We found that day length was an important factor influencing when miscanthus and saccharum flowered. Sugarcane could be induced to flower in a central Illinois greenhouse using supplemental lighting to reduce the rate at which days shortened during the autumn and winter to 1 min d-1, which allowed us to synchronize the flowering of some sugarcane genotypes with Miscanthus genotypes primarily from low latitudes. In a complementary growth chamber experiment, we evaluated 33 miscanthus genotypes, including 28 M. sinensis, 2 M. floridulus, and 3 M. ×giganteus collected from 20.9° S to 44.9° N for response to three day lengths (10 h, 12.5 h, and 15 h). High latitude-adapted M. sinensis flowered mainly under 15 h days, but unexpectedly, short days resulted in short, stocky plants that did not flower; in some cases, flag leaves developed under short days but heading did not occur. In contrast, for M. sinensis and M. floridulus from low latitudes, shorter day lengths typically resulted in earlier flowering, and for some low latitude genotypes, 15 h days resulted in no flowering. However, the highest ratio of reproductive shoots to total number of culms was typically observed for 12.5 h or 15 h days. Latitude of origin was significantly associated with culm length, and the shorter the days, the stronger the relationship. Nearly all entries achieved maximal culm length under the 15 h treatment, but the nearer to the equator an accession originated, the less of a difference in culm length between the short-day treatments and the 15 h day treatment. Under short days, short culms for high-latitude accessions was achieved by different physiological mechanisms for M. sinensis genetic groups from the mainland in comparison to those from Japan; for mainland accessions, the mechanism was reduced internode length, whereas for Japanese accessions the phyllochron under short days was greater than under long days. Thus, for M. sinensis, short days typically hastened floral induction, consistent with the expectations for a facultative short-day plant. However, for high latitude accessions of M. sinensis, days less than 12.5 h also signaled that plants should prepare for winter by producing many short culms with limited elongation and development; moreover, this response was also epistatic to flowering. Thus, to flower M. sinensis that originates from high latitudes synchronously with sugarcane, the former needs day lengths >12.5 h (perhaps as high as 15 h), whereas that the latter needs day lengths <12.5 h.

This upload contains all datasets used in Experiment 2 of the EMMA paper (appeared in WABI 2023): Shen, Chengze, Baqiao Liu, Kelly P. Williams, and Tandy Warnow. "EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment". The zip file has the following structure (presented as an example): salma_paper_datasets/ |_README.md |_10aa/ |_crw/ |_homfam/ |_aat/ | |_... |_... |_het/ |_5000M2-het/ | |_... |_5000M3-het/ ... |_rec_res/ Generally, the structure can be viewed as: [category]/[dataset]/[replicate]/[alignment files] # Categories: 1. 10aa: There are 10 small biological protein datasets within the `10aa` directory, each with just one replicate. 2. crw: There are 5 selected CRW datasets, namely 5S.3, 5S.E, 5S.T, 16S.3, and 16S.T, each with one replicate. These are the cleaned version from Shen et. al. 2022 (MAGUS+eHMM). 3. homfam: There are the 10 largest Homfam datasets, each with one replicate. 4. het: There are three newly simulated nucleotide datasets from this study, 5000M2-het, 5000M3-het, and 5000M4-het, each with 10 replicates. 5. rec\_res: It contains the Rec and Res datasets. Detailed dataset generation can be found in the supplementary materials of the paper. # Alignment files There are at most 6 `.fasta` files in each sub-directory: 1. `all.unaln.fasta`: All unaligned sequences. 2. `all.aln.fasta`: Reference alignments of all sequences. If not all sequences have reference alignments, only the sequences that have will be included. 3. `all-queries.unaln.fasta`: All unaligned query sequences. Query sequences are sequences that do not have lengths within 25% of the median length (i.e., not full-length sequences). 4. `all-queries.aln.fasta`: Reference alignments of query sequences. If not all queries have reference alignments, only the sequences that have will be included. 5. `backbone.unaln.fasta`: All unaligned backbone sequences. Backbone sequences are sequences that have lengths within 25% of the median length (i.e., full-length sequences). 6. `backbone.aln.fasta`: Reference alignments of backbone sequences. If not all backbone sequences have reference alignments, only the sequences that have will be included. >If all sequences are full-length sequences, then `all-queries.unaln.fasta` will be missing. >If fewer than two query sequences have reference alignments, then `all-queries.aln.fasta` will be missing. >If fewer than two backbone sequences have reference alignments, then `backbone.aln.fasta` will be missing. # Additional file(s) 1. `350378genomes.txt`: the file contains all 350,378 bacterial and archaeal genome names that were used by Prodigal (Hyatt et. al. 2010) to search for protein sequences.

This dataset is associated with a larger manuscript published in 2022 in the Illinois Natural History Survey Bulletin that summarized the Fishes of Champaign County project from 2012-2015. With data spanning over 120 years, the Fishes of Champaign County is a comprehensive, long-term investigation into the changing fish communities of east-central Illinois. Surveys first occurred in Champaign County in the late 1880s (40 sites), with subsequent surveys in 1928–1929 (125 sites), 1959–1960 (143 sites), and 1987–1988 (141 sites). Between 2012 and 2015, we resampled 122 sites across Champaign County. The combined data from these five surveys have produced a unique perspective into not only the fish communities of the region, but also insight into in-stream habitat changes during the past 120 years. The dataset is in Microsoft Access format, with five data tables, one for each time period surveyed. Field names are self-explanatory, with some variation in data types collected during different surveys as follows: Forbes & Richardson (1880s) collected presence/absence only. Thompson & Hunt (1928-1929) collected abundance only, Larimore & Smith (1959-1960) collected length and weight for some samples, but only presence/absence at others. In some cases, fish of the same species were weighed in bulk, with the fields “LOW” and “HIGH” indicating the lower and upper limits of total length in the batch, and weight indicating the gross weight of all fish in the batch. Larimore and Bayley (1987-1988) collected length and weight for all surveys, and Sherwood and Stein (2012-2015) collected length and weight for all surveys except for cases where extremely abundant single species where subsampled. Lengths are reported in millimeters, and weight in grams. Two lookup tables provide information about species codes used in the data tables and sample site location and notes.

This dataset contains the first-generation (1st-gen) and second-generation (2nd-gen) citation relationships to a set of focal papers. The 1st-gen citation relationships are the instances of one paper citing a focal paper. These citing papers are called "1st-gen citations." The 2nd-gen citation relationships are the instances that a paper cites a 1st-gen citation. The citing paper in the 2nd-gen citation relationship is a second-generation (2nd-gen) citation. When a 2nd-gen citation is also a 1st-gen citation, it creates a transitive closure with the focal paper. Each focal paper has an abbreviation, which can be found below. The 1st-gen and 2nd-gen citation relationships were extracted from the Curated Open Citation Dataset (Korobskiy & Chacko, 2023), which is derived from a copy of COCI, the OpenCitations Index of Crossref Open DOI-to-DOI Citations, downloaded on May 6, 2023. Scripts used to collect this dataset can be found at https://github.com/yuanxiesa/transitive_closure_study. Each focal paper currently has two files: {abbreviation}_1st.csv contains the 1st-gen citation relationships; {abbreviation}_2nd.csv contains the 2nd-gen citation relationships. Focal paper abbreviation == "louvain": Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 Focal paper abbreviation == "lp": Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(3), 036106. https://doi.org/10.1103/PhysRevE.76.036106 Focal paper abbreviation == "gn": Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. https://doi.org/10.1103/PhysRevE.69.026113

This project investigates retraction indexing agreement among data sources: Crossref, Retraction Watch, Scopus, and Web of Science. As of July 2024, this reassesses the April 2023 union list of Schneider et al. (2023): https://doi.org/10.55835/6441e5cae04dbe5586d06a5f. As of April 2023, over 1 in 5 DOIs had discrepancies in retraction indexing among the 49,924 DOIs indexed as retracted in at least one of Crossref, Retraction Watch, Scopus, and Web of Science (Schneider et al., 2023). Here, we determine what changed in 15 months. Pipeline code to get the results files can be found in the GitHub repository https://github.com/infoqualitylab/retraction-indexing-agreement in the iPython notebook 'MET-STI2024_Reassessment_of_retraction_indexing_agreement.ipynb' Some files have been redacted to remove proprietary data, as noted in README.txt. Among our sources, data is openly available only for Crossref and Retraction Watch. FILE FORMATS: 1) unionlist_completed_2023-09-03-crws-ressess.csv - UTF-8 CSV file 2) unionlist_completed-ria_2024-07-09-crws-ressess.csv - UTF-8 CSV file 3) unionlist-15months-period_sankey.png - Portable Network Graphics (PNG) file 4) unionlist_ria_proportion_comparison.png - Portable Network Graphics (PNG) file 5) README.txt - text file FILE DESCRIPTION: Description of the files can be found in README.txt

Lipids accumulated in the vegetative tissues of cellulosic feedstocks can be a potential raw material for biodiesel and bioethanol production. In this work, bagasse of genetically engineered sorghum was subjected to liquid hot-water pretreatment at 170, 180, and 190 °C for different reaction time. Under the optimal pretreatment condition (170 °C, 20 min), the residue was enriched in glucan (57.39 ± 2.63 % w/w) and xylan (13.38 ± 0.49 % w/w). The total lipid content of the pretreated residue was 6.81% w/w, similar to that observed in untreated bagasse (6.30% w/w). Pretreatment improved the enzymatic digestibility of bagasse, allowing a recovery of 79% w/w and 86% w/w of glucose and xylose, respectively. The pretreatment and enzymatic saccharification resulted in a 2-fold increase in total lipid in enzymatic residue compared to the original bagasse. Thus, pretreatment and enzymatic hydrolysis enabled high sugar recovery while concentrating triglycerides and free fatty acids in the residue.

This set of scripts accompanies the manuscript describing the R package polyRAD, which uses DNA sequence read depth to estimate allele dosage in diploids and polyploids. Using several high-confidence SNP datasets from various species, allelic read depth from a typical RAD-seq dataset was simulated, then genotypes were estimated with polyRAD and other software and compared to the true genotypes, yielding error estimates.

File Name: Inclusion_Criteria_Annotation.csv Data Preparation: Xiaoru Dong Date of Preparation: 2018-12-14 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: The file contains lists of inclusion criteria of Cochrane Systematic Reviews and the manual annotation results. 5420 inclusion criteria were annotated, out of 7158 inclusion criteria available. Annotations are either "Only RCTs" or "Others". There are 2 columns in the file: - "Inclusion Criteria": Content of inclusion criteria of Cochrane Systematic Reviews. - "Only RCTs": Manual Annotation results. In which, "x" means the inclusion criteria is classified as "Only RCTs". Blank means that the inclusion criteria is classified as "Others". Notes: 1. "RCT" stands for Randomized Controlled Trial, which, in definition, is "a work that reports on a clinical trial that involves at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table." [Randomized Controlled Trial publication type definition from https://www.nlm.nih.gov/mesh/pubtypes.html]. 2. In order to reproduce the relevant data to this, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.

The text file contains the original data used in the phylogenetic analyses of Xue et al. (2020: Systematic Entomology, in press). The text file is marked up according to the standard NEXUS format commonly used by various phylogenetic analysis software packages. The file will be parsed automatically by a variety of programs that recognize NEXUS as a standard bioinformatics file format. The first six lines of the file identify the file as NEXUS, indicate that the file contains data for 89 taxa (species) and 2676 characters, indicate that the first 2590 characters are DNA sequence and the last 86 are morphological, that gaps inserted into the DNA sequence alignment and inapplicable morphological characters are indicated by a dash, and that missing data are indicated by a question mark. The file contains aligned nucleotide sequence data for 5 gene regions and 86 morphological characters. The positions of data partitions are indicated in the mrbayes block of commands for the phylogenetic program MrBayes at the end of the file (Subset1 = 16S gene; Subset2 = 28S gene; Subset3 = COI gene; Subset 4 = Histone H3 and H2A genes). The mrbayes block also contains instructions for MrBayes on various non-default settings for that program. These are explained in the original publication. Descriptions of the morphological characters and more details on the species and specimens included in the dataset are provided in the supplementary document included as a separate pdf, also available from the journal website. The original raw DNA sequence data are available from NCBI GenBank under the accession numbers indicated in the supplementary file.

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. This is a repository to save datasets and codes related to this project. Please read and cite the following paper if you would like to use the data: Becker M., Han K., Werthmann A., Rezapour R., Lee H., Diesner J., and Witt A. (2024). Detecting Impact Relevant Sections in Scientific Research. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). This folder contains the following files: evaluation_20220927.ods: Annotated German passages (Artificial Intelligence, Linguistics, and Music) - training data annotated_data.big_set.corrected.txt: Annotated German passages (Mobility) - training data incl_translation_all.csv: Annotated English passages (Artificial Intelligence, Linguistics, and Music) - training data incl_translation_mobility.csv: Annotated German passages (Mobility) - training data ttparagraph_addmob.txt: German corpus (unannotated passages) model_result_extraction.csv: Extracted impact-relevant passages from the German corpus based on the model we trained rf_model.joblib: The random forest model we trained to extract impact-relevant passages Data processing codes can be found at: https://github.com/khan1792/texttransfer

This dataset contains measurements of water loss as white-tailed deer (Odocoileus virginianus) retroypharyngeal lymph nodes air-dried in a refrigerator for 31 days. Daily weights for lymph nodes are recorded every 24 hours, as are the variables "firmness" and "surface wetness". "Firmness" is a categorical variable measuring how much the tissue deforms to the touch (soft, medium, or hard). "Surface wetness" is the amount of visible moisture on the outside of the lymph node (all, some, or none). Lymph node weights were measured until their weights stabilized for 3 consecutive days at two decimal places (ex. 3.02, 3.02, 3.02) or until the weights fluctuated only by 0.01 (ex. 3.02, 3.03, 3.02). Lymph nodes were from northern Illinois white-tailed deer collected as part of the Illinois Department of Natural Resources' ongoing chronic wasting disease (CWD) management efforts.

Creating controlled lipid unsaturation locations in oleochemicals can be a key to many bioengineered products. However, evaluating the effects of modifications to the acyl-ACP desaturase on lipid unsaturation is not currently amenable to high-throughput assays, limiting the scale of redesign efforts to <200 variants. Here, we report a rapid mass spectrometry (MS) assay for profiling the positions of double bonds on membrane lipids produced by Escherichia coli colonies after treatment with ozone gas. By MS measurement of the ozonolysis products of Δ6 and Δ8 isomers of membrane lipids from colonies expressing recombinant Thunbergia alata desaturase, we screened a randomly mutagenized library of the desaturase gene at 5 s per sample. Two variants with altered regiospecificity were isolated, indicated by an increase in 16:1 Δ8 proportion. We also demonstrated the ability of these desaturase variants to influence the membrane composition and fatty acid distribution of E. coli strains deficient in the native acyl-ACP desaturase gene, fabA. Finally, we used the fabA deficient chassis to concomitantly express a non-native acyl-ACP desaturase and a medium-chain thioesterase from Umbellularia californica, demonstrating production of only saturated free fatty acids.

This project investigates retraction indexing agreement in PubMed between 2024-07-03 and 2025-05-09 in order to address an API limitation that resulted in 199 items being excluded from analysis in "Analyzing the consistency of retraction indexing". PubMed was queried on 2024-07-03 and on 2025-05-09 using the search “Retracted Publication[PT]”. PubMed is only able to return 10,000 items when queried via the E-Utilities API. When the pipeline was run 2024-07-03, the search between 2020 and 2024 returned 10,199 items, meaning that an expected 199 items indexed as retracted in PubMed were excluded. This dataset uses and compares information from PubMed as of 2025-05-09 to attempt to identify those 199 items.

Microbial oils are a sustainable biomass-derived substitute for liquid fuels and vegetable oils. Oilcane, an engineered sugarcane with superior feedstock characteristics for biodiesel production, is a promising candidate for bioconversion. This study describes the processing of oilcane stems into juice and hydrothermally pretreated lignocellulosic hydrolysate and their valorization to ethanol and microbial oil using Saccharomyces cerevisiae and engineered Rhodosporidium toruloides strains, respectively. A bioethanol titer of 106 g/L was obtained from S. cerevisiae grown on oilcane juice in a 3 L fermenter, and a lipid titer of 8.8 g/L was obtained from R. toruloides grown on oilcane hydrolysate in a 75 L fermenter. Oil was extracted from the R. toruloides cells using supercritical CO2, and the observed fatty acid profile was consistent with previous studies on this strain. These results demonstrate the feasibility of pilot-scale lipid production from oilcane hydrolysate as part of an integrated bioconversion strategy.

This data set is related to the SoyFACE experiments, which are open-air agricultural climate change experiments that have been conducted since 2001. The fumigation experiments take place at the SoyFACE farm and facility in Champaign County, Illinois during the growing season of each year, typically between June and October. This V4 contains new experimental data files, hourly fumigation files, and weather/ambient files for 2022 and 2023, since the original dataset only included files for 2001-2021. The MATLAB code has also been updated for efficiency, and explanatory files have been updated accordingly. Below are new changes in V4: - The "SoyFACE Plot Information 2001 to 2021" file is renamed to “SoyFACE ring information 2001 to 2023.xlsx”. Data for 2022 and 2023 were added. File contains information about each year of the SoyFACE experiments, including the fumigation treatment type (CO2, O3, or a combination treatment), the crop species, the plots (also referred to as 'rings' and labeled with numbers between 2 and 31) used in each experiment, important experiment dates, and the target concentration levels or 'setpoints' for CO2 and O3 in each experiment. - The "SoyFACE 1-Minute Fumigation Data Files" were updated to contain sub-folders for each year of the experiments (2001-2023), each of which contains sub-folders for each ring used in that year's experiments. This data set also includes hourly data files for the fumigation experiments ("SoyFACE Hourly Fumigation Data Files" folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments ("Hourly Weather and Ambient Data Files" folder which has also been updated to include 2022 and 2023 data). The ambient CO2 and O3 data are collected at SoyFACE, and the weather data are collected from the SURFRAD and WARM weather stations located near the SoyFACE farm. - “Rings.xlsx” is new in this version. This file lists the rings and treatments used in each year of the SoyFACE experiments between 2001 and 2023 and is used in several of the MATLAB codes. - “CMI Weather Data Explanation.docx” is newly added. This file contains specific information about the processing of raw weather data, which is used in the hourly weather and ambient data files. - Files that were in RAR format in V3 are now updated and saved as ZIP format, including: Hourly Weather and Ambient Data Files.zip , SoyFACE 1-Minute Fumigation Data Files.zip , SoyFACE Hourly Fumigation Data Files.zip, and Matlab Files.zip. - The "Fumigation Target Percentages" file was updated to add data for 2022 and 2023. This file shows how much of the time the CO2 and O3 fumigation levels are within a 10 or 20 percent margin of the target levels when the fumigation system is turned on. - The "Matlab Files" folder contains custom code (Aspray, E.K.) that was used to clean the "SoyFACE 1-Minute Fumigation Data" files and to generate the "SoyFACE Hourly Fumigation Data" and "Fumigation Target Percentages" files. Code information can be found in the various "Explanation" files. The Matlab code changes are as follows: 1. “Data_Issues_Finder.m” code was changed to use the “Ring.xlsx” file to gather ring and treatment information based on the contents of the file rather than being hardcoded in the Matlab code itself. 2. “Data_Issues_Finder_all.m” code is new. This code is the same as the “Data_Issues_Finder.m” code except that it identifies all CO2 and O3 repeats. In contrast, the “Data_Issues_Finder.m” code only identifies CO2 and O3 repeats that occur when the fumigation system is turned on. 3. “Target_Yearly.m” code was changed to use the “Ring.xlsx” file to gather ring and treatment information based on the contents of the file rather than being hardcoded in the Matlab code itself. 4. “HourlyFumCode.m” code is new. This code uses the “Rings.xlsx” file to gather ring and treatment information based on the contents of the file instead of the user needing to define these values explicitly. This code also defines a list of all ring folders for the year selected and runs the hourly code for each ring, instead of the user having to run the hourly code for each ring individually. Finally, the code generates two dialog boxes for the user, one which allows user to specify whether they want the hourly code to be run for 1-minute fumigation files or 1-minute ambient files, and another which allows user to specify whether they would like the hourly fumigation averages to be replaced with hourly ambient averages when the fumigation system is turned off. 5. “HourlyDataFun.m” code was changed to run either “HourlyData.m” code or “HourlyDataAmb.m” code, depending on user input in the first dialog box. 6. “HourlyData.m” code was changed to replace hourly fumigation averages with hourly ambient averages when the fumigation system is turned off, depending on user input in the second dialog box. 7. “HourlyDataAmb.m” code is new. This code is similar to “HourlyData.m” code but is used to calculate hourly averages for 1-minute ambient files instead 1-minute fumigation files. 8. “batch.m” code was changed to account for new function input variables in “HourlyDataFun.m” code, along with adding header columns for “FumOutput.xlsx” and “AmbOutput.xlsx” output files generated by “HourlyData.m” and “HourlyDataAmb.m” code. - Finally, the " * Explanation" files contain information about the column names, units of measurement, steps needed to use Matlab code, and other pertinent information for each data file. Some of them have been updated to reflect the current change of data.

This dataset is provided to support the statements in Tarokh, A., and R.Y. Makhnenko. 2019. Remarks on the solid and bulk responses of fluid-filled porous rock, Geophysics. The unjacketed bulk modulus is a poroelastic parameter that can be directly measured in a laboratory test under a loading that preserves the difference between the mean stress and pore pressure constant. For a monomineralic rock, the measurement of the unjacketed bulk modulus is ignored because it is assumed to be equal to the bulk modulus of the solid phase. To examine this assumption, we tested porous sandstones (Berea and Dunnville) and limestones (Apulian and Indiana) mainly composed of quartz and calcite, respectively, under the unjacketed condition. The presence of microscale inhomogeneities, in the form of non-connected (occluded) pores, was shown to cause a considerable difference between the unjacketed bulk modulus and the bulk modulus of the solid phase. Furthermore, we found the unjacketed bulk modulus to be independent of the unjacketed pressure and Terzaghi effective pressure and therefore a constant.