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A set of gene and gene-related entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset is derived from the COCI, the OpenCitations Index of Crossref open DOI-to-DOI references (opencitations.net). Silvio Peroni, David Shotton (2020). OpenCitations, an infrastructure organization for open scholarship. Quantitative Science Studies, 1(1): 428-444. https://doi.org/10.1162/qss_a_00023 We have curated it to remove duplicates, self-loops, and parallel edges. These data were copied from the Open Citations website on May 6, 2023 and subsequently processed to produce a node list and an edge-list. Integer_ids have been assigned to the DOIs to reduce memory and storage needs when working with these data. As noted on the Open Citation website, each record is a citing-cited pair that uses DOIs as persistent identifiers.

Read me file for the data repository ******************************************************************************* This repository has raw data for the publication "Enhancing Carrier Mobility In Monolayer MoS2 Transistors With Process Induced Strain". We arrange the data following the figure in which it first appeared. For all electrical transfer measurement, we provide the up-sweep and down-sweep data, with voltage units in V and conductance unit in S. All Raman modes have unit of cm^-1. ******************************************************************************* How to use this dataset All data in this dataset is stored in binary Numpy array format as .npy file. To read a .npy file: use the Numpy module of the python language, and use np.load() command. Example: suppose the filename is example_data.npy. To load it into a python program, open a Jupyter notebook, or in the python program, run: import numpy as np data = np.load("example_data.npy") Then the example file is stored in the data object. *******************************************************************************

Count histories from camera traps and remotely sensed covariate data used in N-mixture modeling to assess the site use intensity of raccoons in Illinois.

Data from manuscript Atomic-Scale Visualization of a Cascade of Magnetic Orders in the Layered Antiferromagnet GdTe3, to be published in npj Quantum Materials. Powerpoint file has details on how the data can be opened and how the data are labeled.

The synthetic networks in this dataset were generated using the RECCS protocol developed by Anne et al. (2024). Briefly, the RECCS process is as follows. An input network and clustering (by any algorithm) is used to pass input parameters to a stochastic block model (SBM) generator. The output is then modified to improve fit to the input real world clusters after which outlier nodes are added using one of three different options. See Anne et al. (2024): in press Complex Networks and Applications XIII (preprint : arXiv:2408.13647). The networks in this dataset were generated using either version 1 or version 2 of the RECCS protocol followed by outlier strategy S1. The input networks to the process were (i) the Curated Exosome Network (CEN), Wedell et al. (2021), (ii) cit_hepph (https://snap.stanford.edu/), (iii) cit_patents (https://snap.stanford.edu/), and (iv) wiki_topcats (https://snap.stanford.edu/). Input Networks: The CEN can be downloaded from the Illinois Data Bank: https://databank.illinois.edu/datasets/IDB-0908742 -> cen_pipeline.tar.gz -> S1_cen_cleaned.tsv The synthetic file naming system should be interpreted as follows: a_b_c.tsv.gz where a - name of inspirational network, e.g., cit_hepph b - the resolution value used when clustering a with the Leiden algorithm optimizing the Constant Potts Model, e.g., 0.01 c- the RECCS option used to approximate edge count and connectivity in the real world network, e.g., v1 Thus, cit_hepph_0.01_v1.tsv indicates that this network was modeled on the cit_hepph network and RECCSv1 was used to match edge count and connectivity to a Leiden-CPM 0.01 clustering of cit_hepph. For SBM generation, we used the graph_tool software (P. Peixoto, Tiago 2014. The graph-tool python library. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1164194.v14) Additionally, this dataset contains synthetic networks generated for a replication experiment (repl_exp.tar.gz). The experiment aims to evaluate the consistency of RECCS-generated networks by producing multiple replicates under controlled conditions. These networks were generated using different configurations of RECCS, varying across two versions (v1 and v2), and applying the Connectivity Modifier (CM++, Ramavarapu et al. (2024)) pre-processing. Please note that the CM pipeline used for this experiment filters small clusters both before and after the CM treatment. Input Network : CEN Within repl_exp.tar.gz, the synthetic file naming system should be interpreted as follows: cen_<resolution><cm_status><reccs_version>sample<replicate_id>.tsv where: cen – Indicates the network was modeled on the Curated Exosome Network (CEN). resolution – The resolution parameter used in clustering the input network with Leiden-CPM (0.01). cm_status – Either cm (CM-treated input clustering) or no_cm (input clustering without CM treatment). reccs_version – The RECCS version used to generate the synthetic network (v1 or v2). replicate_id – The specific replicate (ranging from 0 to 2 for each configuration). For example: cen_0.01_cm_v1_sample_0.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, CM-treated input, and generated using RECCSv1 (first replicate). cen_0.01_no_cm_v2_sample_1.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, without CM treatment, and generated using RECCSv2 (second replicate). The ground truth clustering input to RECCS is contained in repl_exp_groundtruths.tar.gz.

This datasets provide basis of our analysis in the paper - Potential Impacts of Supersonic Aircraft on Stratospheric Ozone and Climate. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

A set of cell-line entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

Includes two files (.csv) behind all analyses and results in the paper published with the same title. <b>1) 'sites.species.counts'</b> is the raw 2018-2022 data from Angella Moorehouse (Illinois Nature Preserves Commission) including her 456 identified pollinator species and her raw counts per site (there may be a few errors of identification or naming, and there will always be name changes over time). Headers in columns F through Q correspond to the remnant-site labels in Figure 1 and Table 1 of the paper. Columns R to AB are the “nonremnant” sites, which have not been uniquely labelled since the specific sites aren't referenced anywhere in the manuscript. <b>2) 'C.scores'</b> has the 265 species assigned empirical C values (empirical.C) along with the four sets of expert C values and their confidence ranks (low, medium, high), and the Illinois/Indiana conservation ranks (S-ranks), following the methods described in the paper. Other headers in these files: - taxa.code: four-letter abbreviation for genus and specific name - genus: genus name - species: specific epithet - common.name: English name - group: general pollinator taxa group - empirical.C: empirically estimated conservatism score - expert#.C: conservatism score assigned by each of four experts - expert#.conf: expert's confidence in their conservatism score Blank cells in the site-species abundance matrix indicates species absence (or non-detection) Blank cells in C.scores.csv indicates missing S-ranks and unassigned C-scores (with associated missing confidence ranks) where experts lacked knowledge or confidence

Simulated sequences provide a way to evaluate multiple sequence alignment (MSA) methods where the ground truth is exactly known. However, the realism of such simulated conditions often comes under question compared to empirical datasets. In particular, simulated data often does not display heterogeneity in the sequence lengths, a common feature in biological datasets. In order to imitate sequence length heterogeneity, we here present a set of data that are evolved under a mixture model of indel lengths, where indels have an occasional chance of being promoted to long indels (emulating large insertion/deletion events, e.g., domain-level gain/loss). This dataset is otherwise (e.g., in GTR parameters) analogous to the 1000M condition as presented in the SATe paper (doi: 10.1126/science.1171243) but with 5000 sequences and simulated with INDELible (http://abacus.gene.ucl.ac.uk/software/indelible/). For more information, see README.txt. For the INDELible control files, see https://github.com/ThisBioLife/5000M-234-het.

The dataset is for a study conducted to understand genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across four years in one subtropical and three temperate locations and genotyped with 268,109 single nucleotide polymorphisms (SNPs).

This study advances the production of potassium sorbate (KS) from triacetic acid lactone (TAL) utilizing food-grade solvents, ethanol (EtOH) and isopropyl alcohol (IPA). We have previously demonstrated the route to produce KS from TAL in tetrahydrofuran (THF) as the main solvent, but the use of THF is associated with environmental and health risks especially for food applications. The process employs a catalytic approach in food-grade solvents and includes three main steps: hydrogenation, etherification and hydrolysis, and ring-opening hydrolysis to produce KS from TAL. In the synthesis of KS from TAL, the use of IPA leads to higher yields and reduced reaction times compared to EtOH. As a result, the overall reaction time in IPA was reduced to 35.7 h, compared to 42.1 h in our previous study using THF and EtOH, while achieving a comparable KS yield of 84% from TAL. The synthesized KS exhibits a trans-2, trans-4 geometrical configuration, identical to that of commercially available KS. Through techno-economic analysis (TEA) and life cycle assessment (LCA), we estimated full-scale production of KS from sugarcane with the developed process in IPA could achieve a minimum product selling price (MPSP) of $8.27 per kg with a range of $7.06–10.16 per kg [5th–95th percentiles from 6000 Monte Carlo simulations] and a carbon intensity (CI) of 13.7 [9.6–18.6] kg CO2-eq per kg. This study highlights the synthesis of KS from TAL using food-grade solvents, demonstrating improved economic viability and environmental sustainability compared to our previous research (MPSP of $9.68 per kg [$8.47–11.45 per kg] and CI of 16.2 [12.0–21.2] kg CO2-eq per kg), as the total required reaction decreases while achieving the comparable overall yield of KS from TAL.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

Nucleotide sequences from wild parsnip CYP71AJ4 (angelic in synthase. <a href ="https://www.ncbi.nlm.nih.gov/nuccore/EF191021">Genbank EF191021</a>) were obtained by Sanger sequencing. Seeds from individual plants from different populations were harvested to obtain corresponding cDNA. The cDNA was cloned and directly sequenced. Aminoacid translations were obtained using standard codon usage. Alignments of CYP71AJ4 sequences (involved in angular furanocoumarin biosynthesis) with as the reference sequence. Consistent amino acid variabilities were found between some populations. The relationship between sequencing variability and selective pressure is not yet known.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

This dataset contains nucleotide sequences of 16S rRNA gene from phytoplasmas and other bacteria detected in phloem-feeding insects (Hemiptera, Auchenorrhyncha). The datasets were used to compare traditional Sanger sequencing with a next-generation sequencing method, Anchored Hybrid Enrichment (AHE) for detecting and characterizing phytoplasmas in insect DNA samples. The file “Trivellone_etal_SangerSequencing.fas”, comprising 1397 positions (the longest sequence), includes 35 not aligned bacterial 16S rRNA sequences (16 phytoplasmas and 19 other bacterial strains) yielded using Sanger sequencing. The file “Trivellone_etal_AHEmethod1.fas” includes 34 not aligned bacterial 16S rRNA sequences (28 phytoplasmas and 6 other bacterial strains) and it contains 1530 positions (the longest sequence). Each sequence was assembled using assembled based on ABySS v2.1.0 pipeline. The file “Trivellone_etal_AHEmethod2.fas” includes 31 not aligned bacterial 16S rRNA sequences (27 phytoplasmas and 4 other bacterial strains) and it contains 1530 positions (the longest sequence). Each sequence was assembled based on the HybPiper v2.0.1 pipeline . Additional details in the "read_me_trivellone.txt" file attached below.

Bioenergy sorghum (Sorghum bicolor L. Moench) is a promising crop for contributing to the United States bioenergy supply. However, the varying limitations of the marginal lands targeted for its cultivation present a management challenge. This two-year study aimed to investigate how the limitations associated with marginal cropland impact the effects of nitrogen fertilization on the yield of bioenergy sorghum and the uptake of 11 macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Mn, Zn, Cu, and B). The study contrasted prime cropland in central Illinois (Urbana) with three marginal cropland sites in southern (Ewing) and central Illinois (Fairbury and Pesotum). These marginal cropland sites are characterized by varying limitations, including low soil fertility (P and K limitations), leaching and erosion, and flooding, respectively. Four nitrogen rates (0, 56, 112, and 168 kg N ha−1) were tested under eight environments. The average yields and ranges of sorghum biomass were 20.2 (17.0–23.2) Mg ha−1 in Urbana, 18.1 (13.1–19.8) Mg ha−1 in Ewing, 13.8 (9.0–17.3) Mg ha−1 in Fairbury, and 23.3 (14.6–33.0) Mg ha−1 in Pesotum. Optimal N rates were 56 N in Pesotum and 112 N in Urbana, Ewing, and Fairbury. Tissue macronutrient contents in Urbana were generally higher than in the marginal croplands, while micronutrient contents did not show discernible trends. Increasing N rate generally correlated with the macronutrient removal except in Ewing. Comparable sorghum biomass yields were observed between prime and marginal croplands (averaging 18.3 Mg ha−1), but optimal N rates varied between 56 N and 112 N. This suggests that yield gaps can be narrowed by applying the optimal N rates for the respective locations. However, increased removals of macronutrients, especially P and K, with increasing yields indicate the need to revise fertilizer recommendations, particularly for soils deficient in these nutrients. Our study suggests that while sorghum production on marginal cropland is feasible, N management needs to be adapted to the unique limitations associated with various types of marginal cropland.

Data sets to reproduce the results provided by the tutorial in paper "Data-Driven and Machine Learning Based Framework for Image-Guided Single-Cell Mass Spectrometry"

This dataset consists of the secondary ion mass spectrometry (SIMS) depth profiling data that was collected with a Cameca NanoSIMS 50 instrument from a 10 micron by 10 micron region on a Madin-Darby canine kidney (MDCK) cell that had been metabolically labeled so most of its sphingolipids and cholesterol contained the rare nitrogen-15 oxygen-18 isotopes, respectively.

Simulation trajectory data and scripts for Nature Nanotechnology manuscript "A DNA turbine powered by a transmembrane potential across a nanopore" that demonstrates a rationally designed nanoscale DNA-origami turbine with three chiral blades that uses a transmembrane electrochemical potential across a nanopore to drive a DNA bundle into sustained unidirectional rotations of up to 10 revolutions/s. Driven by the asymmetric mobility of a DNA duplex, the rotation direction of the turbine is set by its designed chirality and the salinity of the solvent.

This dataset is associated with the manuscript "Transcriptional responses of detoxification genes to coumaphos in a nontarget species, Galleria mellonella (greater wax moth) (Lepidoptera: Pyralidae), in the beehive environment" This dataset includes 2 Excel files: 1) raw_data_bioassay.xlsx: this file contains the raw data for waxworm bioassay. There are 2 worksheets within this file: - LC50: raw data for measuring LC50 in the laboratory and field strain of Galleria mellonella. - RGR: Relative Growth Rate, raw data for measuring body weight of field strain of Galleria mellonella . 2) raw-data_RT-qPCR.xlsx: this file contains raw data (Ct value) of RT-qPCR.

Data sets from "Inferring Species Trees from Gene-Family with Duplication and Loss using Multi-Copy Gene-Family Tree Decomposition." It contains trees and sequences simulated with gene duplication and loss under a variety of different conditions. <b>Note:</b> - trees.tar.gz contains the simulated gene-family trees used in our experiments (both true trees from SimPhy as well as trees estimated from alignements). - sequences.tar.gz contains simulated sequence data used for estimating the gene-family trees as well as the concatenation analysis. - biological.tar.gz contains the gene trees used as inputs for the experiments we ran on empirical data sets as well as species trees outputted by the methods we tested on those data sets. - stats.txt list statistics (such as AD, MGTE, and average size) for our simulated model conditions.

This study evaluates the bioethanol potential in response to irrigation (IR) and non-irrigation (NIR) of oilcane (OC) during a seasonal drought prior harvest. The juice was extracted through mechanical pressing of stems and fermented by Ethanol Red® yeast to produce first-generation bioethanol. Hydrothermal pretreatment followed by enzymatic hydrolysis of bagasse was performed to produce monomeric sugars from structural carbohydrates. The hydrolysates were fermented with engineered yeast for second-generation bioethanol production. The irrigated oilcane juice (276.3 ± 8.9 g/L) constitutes higher sugar concentrations than non-irrigated oilcane juice (236.5 ± 2.2 g/L). The enzymatic hydrolysis of IR-OC and NIR-OC pretreated bagasse yielded similar concentrations of 247.5 ± 2.22 and 249.7 ± 4.98 g/L fermentable sugars. Industry-relevant bioethanol titers of ≥99 g/L and ≥75 g/L were achieved from juice and hydrolysates, respectively. Therefore, the non-irrigation regime did not impact the 1G and 2G bioethanol titers. However, the overall bioethanol yield can be lower due to the reduction of stem yield (8 %) per hectare.

This dataset includes neuronal development of the Caenorhabditis elegans dauer and adult.