cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c56m33f4q
title: MassIVE MSV000094017 - XLMS Analysis of the proteins affinity-purified from HEK293T cells stably expressing Halo-FAM60A or Halo-SAP30L and crosslinked with DSSO
title: Protein crosslinking | Briefly, cell lysates expressing Halo-Fam60A were mixed with Magne HaloTag Beads, using 100 ul of bead slurry, and incubated overnight at 4C. The beads were then separated with a magnetic concentrator and washed four times using a buffer composed of 10 mM HEPES (pH 7.5), 1.5 mM MgCl2, 0.3 M NaCl, 10 mM KCl, and 0.2% Triton X-100. To elute bound proteins, the beads were treated with 200 ul of a buffer containing 50 mM HEPES (pH 7.5), 0.5 mM EDTA, 1 mM DTT, and 30 units of AcTEV, and incubated for a minimum of 2 h at 4C. The eluate was collected, and proteins were cross-linked using 5 mM disuccinimidylsulfoxide (DSSO; Sigma-Aldrich) for 40 minutes at room temperature. The cross-linking process was quenched by adding 50 mM NH4HCO3 and incubating for 15 minutes at room temperature. LC-MS3 XLMS data acquisition | Crosslinked peptides were analyzed on an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific) coupled to a Dionex UltiMate 3000 RSCLnano System. Peptides were loaded on an Acclaim PepMap 100 C18 0.3-mm-i.d., 5-mm-long trap cartridge (Thermo Fisher Scientific), then eluted to an analytical column (50 um i.d., 150 mm long) packed in-house with ReproSil-Pur C18-AQ 1.9-um resin (Dr. Masch GmbH). Full MS scans were performed at 60,000-m/z resolution in the orbitrap with a 1.6-m/z isolation window and a scan range from 375 to 1,500 m/z. The top three peptides with charge states from +4 to +8 were selected for MS2 fragmentation with 20% CID energy. MS2 scans were detected in the Orbitrap with 30,000-m/z resolution and a dynamic exclusion time of 40 s. Among MS2 fragments, if two peptides with an exact mass difference of 31.9720 with 20 ppm mass tolerance were present, both were selected for MS3 fragmentation at CID energy of 35%. MS3 scans were performed in the ion trap at rapid scan with an isolation window of 3 m/z and a maximum ion injection time of 200 ms. Each MS2 scan was followed by four MS3 scans. XLMS data analysis | The XlinkX node of Proteome Discoverer 3.0 (Thermo Fisher Scientific) was used to search for crosslinked peptides in the XLMS dataset by identifying MS2 scans with reporter ions characteristic of DSSO-modified lysine residues, with mass shifts of 158.00376 Da (monoisotopic mass), 54.01056 Da (alkene fragment), and 85.98264 Da (thiol fragment). In addition, the Sequest HT node searched the dataset with the following settings: 10 ppm precursor ion mass tolerance; 0.6 Da fragment ion mass tolerance; fixed modification: Cys carbamidomethylation; variable modifications: Met oxidation, N-terminal acetylation, Lys with amidated DSSO (279.07766 Da), and Lys with hydrolyzed DSSO (176.01433 Da); 3 maximum number of dynamic modifications. The data was searched against a non-redundant protein sequence database assembled from NCBI RefSeq GRCh38.p14 and T2T-CHM13v2.0 (2022-04-12 release), complemented with 428 common contaminants. The protein false discovery rate was set at 0.01.
DOI: 10.5281/zenodo.10621559
title: Measuring spatial navigation during locomotion in children: a systematic review
DOI: 10.5281/zenodo.10621560
title: Measuring spatial navigation during locomotion in children: a systematic review
DOI: 10.6084/m9.figshare.24451669
title: OCS alignment and photoionization calculations

description: OCS alignment and photoionization calculations (2021 - 2023)To accompany the manuscript Woodhouse, J. et al. (2023) ‘Quantitative analysis of aligned molecule photoelectron angular distributions’, In Preparation.This dataset contains the following subsets:<b>electronic_structure</b>: all Gamess files, plus Molden format versions.<b>OCS_ADMs_28K_VM_070722</b>: alignment results (sets of ADMs) as used in the manuscript (300fs, 4.2TW, 28K). See the processing notebooks below for usage.<b>ePolyScat_equilibrium_geom</b>: ePolyScat calculations at the equilibrium geometry. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions. (NOTE: orbitals in this dataset are labelled incorrectly as 8-11 (instead of 9-12) in dirs and files, check ePS input files under 'OrbOcc' keyword for correct orbital occupations if in doubt.)<b>ePolyScat_bondscans</b>: ePolyScat calculations for a range of geometries (orb12), and at 20% stretched only for orbs9-11. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions.<b>OCS_ePS_LF_data_120822</b>: simple tabulations of the ePolyScat cross-sections and B2 parameters corresponding to the LF results at equilibrium geometry.<b>OCS_ePS_LF_data_20pc_020224</b>: simple tabulations of the ePolyScat cross-sections and B2 parameters corresponding to the LF results at 20% stretched geometry.<b>OCS_AFBLM_010224</b>: simple tabulations of the AFBLM results for the equilibrium geometry, data matches the interactive plots detailed below.<b>OCS_AFBLM_20pc_020224</b>: simple tabulations of the AFBLM results for the 20% stretched geometry, data match the interactive plots included in the archive.<br>HTML versions are also available on the web at https://phockett.github.io/ePSdata/OCS-preliminary/. For an overview of the equilibrium geometry results, interactive plots as a function of orbital and (l,m) can be found here, these match the demo plots shown in the root of this repo. (Or see file <i>OCS_orbs8-11_AFBLMs_VM-ADMs_140122-JAKE_tidy-replot-200722_v5.html</i> in the <i>ePolyScat_equilibrium_geom </i>dataset.) For an overview of the bond-scans for orb12, see here (or file <i>OCS_orb12_bondScan_160523-replot_301023.html</i> in the <i>ePolyScat_bondscans</i><i> </i>dataset).Additional simulations of the experimental data based on these results can be found in the dataset: Woodhouse, Joanne; Hockett, Paul; Makhija, Varun (2024). <i>Experimental data and Theoretical Alignment Frame PAD notebooks</i>. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25144469The full collection can be found on the project page <i>OCS alignment and photoionization (project)</i> https://figshare.com/projects/OCS_alignment_and_photoionization_project_/194090Cite as: Hockett, P., Makhija, V. and Woodhouse, J. (2023) ‘OCS alignment and photoionization calculations’. Figshare. https://doi.org/10.6084/m9.figshare.24451669.<br>
DOI: 10.6084/m9.figshare.24451669.v5
title: OCS alignment and photoionization calculations

description: OCS alignment and photoionization calculations (2021 - 2023)To accompany the manuscript Woodhouse, J. et al. (2023) ‘Quantitative analysis of aligned molecule photoelectron angular distributions’, In Preparation.This dataset contains the following subsets:<b>electronic_structure</b>: all Gamess files, plus Molden format versions.<b>OCS_ADMs_28K_VM_070722</b>: alignment results (sets of ADMs) as used in the manuscript (300fs, 4.2TW, 28K). See the processing notebooks below for usage.<b>ePolyScat_equilibrium_geom</b>: ePolyScat calculations at the equilibrium geometry. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions. (NOTE: orbitals in this dataset are labelled incorrectly as 8-11 (instead of 9-12) in dirs and files, check ePS input files under 'OrbOcc' keyword for correct orbital occupations if in doubt.)<b>ePolyScat_bondscans</b>: ePolyScat calculations for a range of geometries (orb12), and at 20% stretched only for orbs9-11. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions.<b>OCS_ePS_LF_data_120822</b>: simple tabulations of the ePolyScat cross-sections and B2 parameters corresponding to the LF results at equilibrium geometry.<b>OCS_ePS_LF_data_20pc_020224</b>: simple tabulations of the ePolyScat cross-sections and B2 parameters corresponding to the LF results at 20% stretched geometry.<b>OCS_AFBLM_010224</b>: simple tabulations of the AFBLM results for the equilibrium geometry, data matches the interactive plots detailed below.<b>OCS_AFBLM_20pc_020224</b>: simple tabulations of the AFBLM results for the 20% stretched geometry, data match the interactive plots included in the archive.<br>HTML versions are also available on the web at https://phockett.github.io/ePSdata/OCS-preliminary/. For an overview of the equilibrium geometry results, interactive plots as a function of orbital and (l,m) can be found here, these match the demo plots shown in the root of this repo. (Or see file <i>OCS_orbs8-11_AFBLMs_VM-ADMs_140122-JAKE_tidy-replot-200722_v5.html</i> in the <i>ePolyScat_equilibrium_geom </i>dataset.) For an overview of the bond-scans for orb12, see here (or file <i>OCS_orb12_bondScan_160523-replot_301023.html</i> in the <i>ePolyScat_bondscans</i><i> </i>dataset).Additional simulations of the experimental data based on these results can be found in the dataset: Woodhouse, Joanne; Hockett, Paul; Makhija, Varun (2024). <i>Experimental data and Theoretical Alignment Frame PAD notebooks</i>. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25144469The full collection can be found on the project page <i>OCS alignment and photoionization (project)</i> https://figshare.com/projects/OCS_alignment_and_photoionization_project_/194090Cite as: Hockett, P., Makhija, V. and Woodhouse, J. (2023) ‘OCS alignment and photoionization calculations’. Figshare. https://doi.org/10.6084/m9.figshare.24451669.<br>
DOI: 10.6084/m9.figshare.24451669.v4
title: OCS alignment and photoionization calculations

description: OCS alignment and photoionization calculations (2021 - 2023)To accompany the manuscript Woodhouse, J. et al. (2023) ‘Quantitative analysis of aligned molecule photoelectron angular distributions’, In Preparation.This dataset contains the following subsets:<b>electronic_structure</b>: all Gamess files, plus Molden format versions.<b>OCS_ADMs_28K_VM_070722</b>: alignment results (sets of ADMs) as used in the manuscript (300fs, 4.2TW, 28K). See the processing notebooks below for usage.<b>ePolyScat_equilibrium_geom</b>: ePolyScat calculations at the equilibrium geometry. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions. (NOTE: orbitals in this dataset are labelled incorrectly as 8-11 (instead of 9-12) in dirs and files, check ePS input files under 'OrbOcc' keyword for correct orbital occupations if in doubt.)<b>ePolyScat_bondscans</b>: ePolyScat calculations for a range of geometries (orb12), and at 20% stretched only for orbs9-11. Sample processing Jupyter Python notebooks are also included, for a quick overview of the results check the HTML versions.<b>OCS_ePS_LF_data_120822</b>: simple tabulations of the ePolyScat cross-sections and B2 parameters corresponding to the LF results at equilibrium geometry.<b>OCS_AFBLM_010224</b>: simple tabulations of the AFBLM results for the equilibrium geometry, data matches the interactive plots detailed below.<br>HTML versions are also available on the web at https://phockett.github.io/ePSdata/OCS-preliminary/. For an overview of the equilibrium geometry results, interactive plots as a function of orbital and (l,m) can be found here, these match the demo plots shown in the root of this repo. (Or see file <i>OCS_orbs8-11_AFBLMs_VM-ADMs_140122-JAKE_tidy-replot-200722_v5.html</i> in the <i>ePolyScat_equilibrium_geom </i>dataset.) For an overview of the bond-scans for orb12, see here (or file <i>OCS_orb12_bondScan_160523-replot_301023.html</i> in the <i>ePolyScat_bondscans</i><i> </i>dataset).<br>Cite as: Hockett, P. and Makhija, V. (2023) ‘OCS alignment and photoionization calculations’. Figshare. https://doi.org/10.6084/m9.figshare.24451669.
DOI: 10.13012/b2idb-4447017_v1
title: Red Fox (Vulpes vulpes) Y-Chromosome Sequence

description: This dataset contains raw data associated with the red fox Y-chromosome assembly (see https://doi.org/10.3390/genes10060409). It includes a fasta file of the 171 scaffolds from the red fox reference genome assembly identified as likely to contain Y-chromosome sequence, the raw BLAST results, and the ABySS assemblies described in the manuscript.
DOI: 10.13012/b2idb-9440404_v1
title: Frequent pattern subject transactions from the University of Illinois Library (2016 - 2018)

description: The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.
DOI: 10.13012/b2idb-9733959_v1
title: Data for Dynamic controls on field-scale soil nitrous oxide hot spots and hot moments across a microtopographic gradient

description: This dataset includes all data presented in the manuscript entitled: "Dynamic controls on field-scale soil nitrous oxide hot spots and hot moments across a microtopographic gradient"
DOI: 10.13012/b2idb-3352362_v1
title: Pradhan 2019 Data

description: Data necessary for production of figures presented in "Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models" by Pradhan et al.
DOI: 10.13012/b2idb-0569467_v2
title: Data from: Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge

description: This repository includes scripts and datasets for the paper, "Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge." All data files in this repository are for analyses using the logdet distance matrix computed on the concatenated alignment. Data files for analyses using the average gene-tree internode distance matrix can be downloaded from the Illinois Data Bank (https://doi.org/10.13012/B2IDB-1424746_V1). The latest version of NJMerge can be downloaded from Github (https://github.com/ekmolloy/njmerge).<br>

<strong>List of Changes:</strong>
• Updated timings for NJMerge pipelines to include the time required to estimate distance matrices; this impacted files in the following folder: <strong>data.zip</strong>
• Replaced "Robinson-Foulds" distance with "Symmetric Difference"; this impacted files in the following folders: <strong> tools.zip; data.zip; scripts.zip</strong>
• Added some additional information about the java command used to run ASTRAL-III; this impacted files in the following folders: <strong>data.zip; astral64-trees.tar.gz (new)</strong>
DOI: 10.13012/b2idb-4085385_v1
title: Spatial and Temporal Allocation of Ammonia Emissions from Fertilizer Application Important for Air Quality Predictions in U.S. Corn Belt

description: This dataset contains scripts and data developed as a part of the research manuscript titled “Spatial and Temporal Allocation of Ammonia Emissions from Fertilizer Application Important for Air Quality Predictions in U.S. Corn Belt”. This includes (1) Spatial and temporal factors for ammonia emissions from agricultural fertilizer usage developed using the hybrid ISS-DNDC method for the Midwest U.S., (2) CAMx job scripts and outputs of predictions of ambient ammonia and total and speciated PM2.5, (3) Observation data used to statistically evaluate CAMx predictions, and (4) MATLAB programs developed to pair CAMx predictions with ground-based observation data in space and time.
DOI: 10.22003/xfel.eu-data-900424-00
title: TST SASE1 DAQ tests
DOI: 10.13012/b2idb-0569467_v1
title: Data from: Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge

description: This repository includes scripts and datasets for the paper, "Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge." All data files in this repository are for analyses using the logdet distance matrix computed on the concatenated alignment. Data files for analyses using the average gene-tree internode distance matrix can be downloaded from the Illinois Data Bank (https://doi.org/10.13012/B2IDB-1424746_V1). The latest version of NJMerge can be downloaded from Github (https://github.com/ekmolloy/njmerge).

***When downloading datasets, please note that the following errors.***

In tools.zip, the compare_trees.py and the compare_tree_lists.py scripts incorrectly refer to the normalized symmetric difference as the normalized Robinson-Foulds distance. Because the normalized symmetric difference and the normalized Robinson-Foulds distance are equal for binary trees, this does not impact the species tree error rates reported in the study. This can impact the gene tree error rates reported in the study (see data-gene-trees.csv in data.zip), as FastTree-2 returns trees with polytomies whenever 3 or more sequences in the input alignment are identical. Note that the symmetric difference rate is always greater than or equal to the Robinson-Foulds error rate, so the gene tree error rates reported in the study are more conservative.
DOI: 10.13012/b2idb-1424746_v1
title: NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees

description: This repository includes scripts, datasets, and supplementary materials for the study, "NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees", presented at RECOMB-CG 2018. The supplementary figures and tables referenced in the main paper can be found in njmerge-supplementary-materials.pdf. The latest version of NJMerge can be downloaded from Github: https://github.com/ekmolloy/njmerge.

***When downloading datasets, please note that the following errors.***

In README.txt, lines 37 and 38 should read:
 + fasttree-exon.tre contains lines 1-25, 1-100, or 1-1000 of fasttree-total.tre
 + fasttree-intron.tre contains lines 26-50, 101-200, or 1001-2000 of fasttree-total.tre
Note that the file names (fasttree-exon.tre and fasttree-intron.tre) are swapped.

In tools.zip, the compare_trees.py and the compare_tree_lists.py scripts incorrectly refer to the "symmetric difference error rate" as the "Robinson-Foulds error rate". Because the normalized symmetric difference and the normalized Robinson-Foulds distance are equal for binary trees, this does not impact the species tree error rates reported in the study. This could impact the gene tree error rates reported in the study (see data-gene-trees.csv in data.zip), as FastTree-2 returns trees with polytomies whenever 3 or more sequences in the input alignment are identical. Note that the normalized symmetric difference is always greater than or equal to the normalized Robinson-Foulds distance, so the gene tree error rates reported in the study are more conservative.

In njmerge-supplementary-materials.pdf, the alpha parameter shown in Supplementary Table S2 is actually the divisor D, which is used to compute alpha for each gene as follows.
1. For each gene, a random value X between 0 and 1 is drawn from a uniform distribution.
2. Alpha is computed as -log(X) / D, where D is 4.2 for exons, 1.0 for UCEs, and 0.4 for introns (as stated in Table S2).
Note that because the mean of the uniform distribution (between 0 and 1) is 0.5, the mean alpha value is -log(0.5) / 4.2 = 0.16 for exons, -log(0.5) / 1.0 = 0.69 for UCEs, and -log(0.5) / 0.4 = 1.73 for introns.
DOI: 10.13012/b2idb-9570561_v1
title: Data from: TreeMerge: A new method for improving the scalability of species tree estimation methods

description: This repository includes scripts and datasets for the paper, "TreeMerge: A new method for improving the scalability of species tree estimation methods." The latest version of TreeMerge can be downloaded from Github (https://github.com/ekmolloy/treemerge).
DOI: 10.5281/zenodo.10621685
title: pseudomugil_signifer_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10621686
title: pseudomugil_signifer_spades_01.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/7564796
title: Biking Thrills India
DOI: 10.13012/b2idb-2044905_v1
title: Flight Ability of Juvenile Songbirds at Fledgling: Examples of Fledgling Drop Tests

description: This data publication provides example video clips related to research on association among flight ability of juvenile songbirds at fledging and juvenile morphological traits (wing emergence, wing length, body condition, mass, and tarsus length. File names reflect the species dropped in each video. These videos are supplemental material for scientific publications by the authors and reflect an example subset of all videos collected form 2017-2018 as part of a larger study on the post-fledging ecology of grassland and shrubland birds in east-Central Illinois, USA. No birds were harmed/injured in the production of these videos and procedures were approved by the Illinois Institutional Animal Care and Use Committee (IACUC), protocol no. 18221. Individuals depicted in the videos have given consent for the videos to be shared (talent/model release form; https://publicaffairs.illinois.edu/resources/release/)
DOI: 10.13012/b2idb-2887291_v1
title: Spatial Conservation and Investment Portfolios to Manage Climate-Related Risk
DOI: 10.13012/b2idb-7478121_v2
title: Experimental data on bulk and unjacketed moduli of porous rocks

description: 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.
DOI: 10.25345/c52v2cn0k
title: MassIVE MSV000094018 - NIOZ DOM analysis - SEALINK 2021 Subset
title: DOM analysis of coral reef & coastal water metabolites at NIOZ Texel
DOI: 10.5517/ccdc.csd.cc2h6866
title: CCDC 2299927: Experimental Crystal Structure Determination

description: Related Article: peiyan zhu|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h6f27
title: CCDC 2300078: Experimental Crystal Structure Determination

description: Related Article: biswaranjan baliarsingh|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.