cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7486184
title: Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset

description: <b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.
description: This research was funded in part by the National Science Centre in Poland (grant number 2020/39/B/NZ2/03127 to F.S.)
DOI: 10.5281/zenodo.7486222
title: Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset

description: <b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.
description: This research was funded in part by the National Science Centre in Poland (grant number 2020/39/B/NZ2/03127 to F.S.)
DOI: 10.5281/zenodo.7486183
title: Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset

description: <b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.
description: This research was funded in part by the National Science Centre in Poland (grant number 2020/39/B/NZ2/03127 to F.S.)
DOI: 10.5281/zenodo.7761080
title: Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset

description: <b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.
description: This research was funded in part by the National Science Centre in Poland (grant number 2020/39/B/NZ2/03127 to F.S.)
DOI: 10.6084/m9.figshare.21743591.v2
title: Tmem43-MOUSE_Suppl. Materials-AJPHeart.docx

description: <strong>SUPPLEMENTARY MATERIAL</strong> <strong>Supplementary Tables S1-S6</strong> <strong>Supplementary Figures legends</strong> <strong>Supplementary Figures S1-S4 </strong> <br>
DOI: 10.5281/zenodo.7761119
title: hartigia_nigra_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7761118
title: hartigia_nigra_spades_01.fasta.gz

description: .
DOI: 10.17630/7959a81e-161d-4ada-9914-08d3d235ce88
title: Investigation of Zeolitic Imidazolate Frameworks using 13C and 15N Solid-State NMR Spectroscopy (dataset)
DOI: 10.58131/es1r-5y81/w1fuwb
title: Recursos electrónicos suscritos 2020

description: Datos de uso de los recursos electrónicos suscritos por la Dirección Nacional de Bibliotecas para la vigencia 2020.
DOI: 10.58131/es1r-5y81/w1fuwb/vzefvn
title: Reporte_General_2020 (5).tab

description: Hoja de cálculo formato .xlxs (2.44 MB)
DOI: 10.17630/599b513d-b173-425c-ac2c-0ccfc50d8798
title: Synthesis and activation for catalysis of Fe-SAPO-34 prepared using iron polyamine complexes as structure directing agents (dataset)
DOI: 10.25625/aysfwa
title: Newsletter No.4

description: Newsletter No.4, Dec 2023
DOI: 10.25625/aysfwa/hakwer
title: Newsletter 4.v2.pdf

description: :unav
DOI: 10.25625/aysfwa/xmdkvc
title: Newsletter 4_2023.pdf

description: :unav
DOI: 10.17630/f0bbc64e-3064-40ac-916b-6ba9dad4715f
title: Homochiral Emissive Λ8- and Δ8-[Ir8Pd4]16+ Supramolecular Cages (dataset)
DOI: 10.18112/openneuro.ds004926.v1.3.0
title: Reliability 3T fMRI experiment
DOI: 10.17630/5b25b1fa-9f10-492f-8b93-b0eeff2cdf77
title: A critical crosscatalytic relationship determines the outcome of competition in a replicator network (dataset)
DOI: 10.57745/bc5ygp
title: SmartSensData.xlsx

description: Analysis based on time series of textual data and satellite images.
DOI: 10.57745/ly1ton
title: SmartSensDescriptionFr.pdf

description: Description des données
DOI: 10.57745/tk44n1
title: SmartSensDescriptionGb.pdf

description: Data Description
DOI: 10.17630/b5ebd26e-dd89-45ca-8478-e9d2f08e5975
title: Data underpinning: Light-sheet microscopy with attenuation-compensated propagation-invariant beams

description: The data will be available soon. In the meantime, if you would like to access the dataset, please, email research-data@st-andrews.ac.uk.
DOI: 10.17630/9aa92a84-6fa4-4f72-92be-1d409a5f2c6b
title: A highly enantioselective alkene methoxycarbonylation enables a concise synthesis of (S)-flurbiprofen. (dataset)

description: NMR spectra of substrates and products.
DOI: 10.17630/cab0e1d8-d12a-4f73-a692-01dcd8aae516
title: Air Exposure Induced Recombination in PTB7:PC71BM Solar Cells (Dataset)
DOI: 10.6084/m9.figshare.25029263
title: 20240120_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.25029263.v1
title: 20240120_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10534334
title: LIME Cimel spectral lunar irradiance dataset

description: In the frame of the Lunar Irradiance Model of ESA (LIME - https://calvalportal.ceos.org/lime) project, spectral irradiance measurements of the Moon are taken at the Izaña and Teide Peak high altitude observatories, located in Tenerife, Spain, and managed by the Meteorological Agency of Spain. The observations are carried out with a Cimel 318T radiometer and started in March 2018 and finished in November 2022.

The Cimel 318T is a filter radiometer, widely used as it is the standard instrument of the AERONET network (http://aeronet.gsfc.nasa.gov/). It is equipped with Silicon and InGaAs detectors and 9 narrow interference filters: 340, 380, 440, 500, 675, 870, 936, 1020, 1640 nm (nominal wavelengths). Since March 2022, the ultraviolet filters 340nm and 380nm were replaced by 550nm and 780nm filters for enhanced lunar observations. The data included in this dataset are extra-terrestrial lunar irradiance obtained through Langley plot method.

The radiometer was calibrated in absolute irradiance at the National Physical Laboratory in order to provide lunar irradiance in Wm-2nm-1 at the indicated spectral channels. More detailed information, including uncertainty of these measurements, is described in Toledano et al. (2023).

The data are provided in (self-describing) NetCDF format. More information is available at https://calvalportal.ceos.org/lime.
DOI: 10.17630/fa79eead-de1e-47ad-85d7-649b5e482f22
title: Green Perovskite Distributed Feedback Lasers (dataset)
DOI: 10.17630/724e018e-e098-4bdd-a963-51044931e379
title: Real-time observation of conformational switching in single conjugated polymer chains (dataset)

description: Single-molecule microscopy raw data in .tiff format of the PL intensity of single surface-anchored, triethoxysilane-terminated poly(3-hexylthiophene) (TES-P3HT) chains. The data has been separated in different sets depending on the molecular weight of the sample (TES-P3HT-S and TES-P3HT-L) and on the kind of experiment, as described in the manuscript 'Real-time observation of conformational switching in single conjugated polymer chains'.
DOI: 10.17630/ce567640-f8d9-4599-a8ed-bcd9a50c1b2a
title: Bull, bear, or rat markets: Rat ‘stock market’ task reveals human-like behavioral biases (dataset)

description: Data for paper in Journal of Neuroscience, Psychology, and Economics
DOI: 10.25405/data.ncl.12721631
title: GC/MS and XPS Dataset Comparing Untreated and Hydous Pyrolysed biopolymers

description: For data underpinning the research paper "Generation of long-chain fatty acids by hydrogen-driven bicarbonate reduction in ancient alkaline hydrothermal vents", please go to https://doi.org/10.25405/data.ncl.23576256.<br>XPS and GC/MS analysis of plant and fungal structural biopolymers used to produce manuscript entitled.Comparison of The Molecular Composition of Plant and Fungal Structural Biopolymer Standards with The Organic Material in Early Cretaceous Ontong Java Plateau Tuff<br>This dataset includes results from replicate experimentsdata in native format and XPS requires CASAXPS andGC/MS requires OpnechromFilename AR=Untreated and hypy=hydrous pyrolysed samples
DOI: 10.25405/data.ncl.12721631.v1
title: GC/MS and XPS Dataset Comparing Untreated and Hydous Pyrolysed biopolymers

description: For data underpinning the research paper "Generation of long-chain fatty acids by hydrogen-driven bicarbonate reduction in ancient alkaline hydrothermal vents", please go to https://doi.org/10.25405/data.ncl.23576256.<br>XPS and GC/MS analysis of plant and fungal structural biopolymers used to produce manuscript entitled.Comparison of The Molecular Composition of Plant and Fungal Structural Biopolymer Standards with The Organic Material in Early Cretaceous Ontong Java Plateau Tuff<br>This dataset includes results from replicate experimentsdata in native format and XPS requires CASAXPS andGC/MS requires OpnechromFilename AR=Untreated and hypy=hydrous pyrolysed samples
DOI: 10.18112/openneuro.ds004937.v1.0.0
title: Effects of a seven-week running intervention with moderate intensity on the volume of the hippocampus and depressive symptoms in young men from the general population.
DOI: 10.6084/m9.figshare.25021730
title: Demultiplexed sequencing reads of barcodes from gDNA associated with scRNAseq experiment - split across 3 sequencing runs

description: Sequencing reads of barcodes isolated from genomic DNA. Only samples with AJF009 identifier pertain to this project. Raw, unprocessed sequencing reads. Associated manuscript: https://www.biorxiv.org/content/10.1101/2023.03.24.534152v1.full
DOI: 10.6084/m9.figshare.25021730.v1
title: Demultiplexed sequencing reads of barcodes from gDNA associated with scRNAseq experiment - split across 3 sequencing runs

description: Sequencing reads of barcodes isolated from genomic DNA. Only samples with AJF009 identifier pertain to this project. Raw, unprocessed sequencing reads. Associated manuscript: https://www.biorxiv.org/content/10.1101/2023.03.24.534152v1.full
DOI: 10.5281/zenodo.10534484
title: Hollardia_meadi_USNMAG9RL06_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10534485
title: Hollardia_meadi_USNMAG9RL06_spades_01.fasta.gz

description: .
DOI: 10.17630/4f1b2967-4c82-44be-a179-b6e28afd328e
title: Data Underpinning: "Depth-resolved multimodal imaging: Wavelength Modulated Spatially Offset Raman Spectroscopy with Optical Coherence Tomography"

description: original data and figures for this paper
DOI: 10.5519/qd.gzf4lk3s
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (690 records)
DOI: 10.17630/55734a8c-cebb-4a38-9175-c9288c68ec83
title: Data underpinning: N- to C-Sulfonyl Photoisomerisation of Dihydropyridinones: A Synthetic and Mechanistic Study
DOI: 10.21223/xw8a1d/d7efd1
title: Module D Crops_information VNM.tab

description: :unav
DOI: 10.21223/xw8a1d/hurbif
title: Module B Plot_information VNM.tab

description: :unav
DOI: 10.21223/xw8a1d/oafklm
title: Module A Preliminary_Information VNM.tab

description: :unav
DOI: 10.21223/xw8a1d/4h5iem
title: Module E Variety_information VNM.tab

description: :unav
DOI: 10.21223/xw8a1d/rg5lbt
title: Module C Plot_Use of land VNM.tab

description: :unav
DOI: 10.21223/xw8a1d
title: Dataset for: Utilization of Participatory Mapping Tools for the Assessment of Crop Diversity in Vietnam

description: A total of 200 farmers in Vietnam, were surveyed utilizing ArcGIS Survey 123. Each farmer's field was physically visited, and the 'Participatory Mapping of Farmers' Fields' tool was employed to delineate polygons for each 'parcel', accurately determining their respective areas. This process captured the characteristics of 773 mapped plots across 200 farmers' households. The resultant comprehensive dataset comprises five distinct databases: A) Preliminary Information, B) Plot Information, C) Plot and Land Use, D) Crop Information, E) Variety Names. These datasets have been individualized and standardized using the common identifiers 'GlobalID Household' and 'GlobalID Plot'.
DOI: 10.21223/xw8a1d/8yftg2
title: Dictionary PGIS Vietnam VNM1.xlsx

description: :unav
DOI: 10.17630/738da16e-b040-49c9-b724-f38af75d803c
title: Three-dimensional photonic confinement in imprinted liquid crystalline pillar microcavities (dataset)
DOI: 10.17630/d520972f-5d27-4b97-bc23-1abfaa91bf3d
title: Exciton dynamics in solid-state green fluorescent protein (dataset)
DOI: 10.7298/28ys-5n97
title: Data from: Midbrain neurons important for the production of mouse ultrasonic vocalizations are not required for distress calls
DOI: 10.17630/651cf473-e525-41ee-8a6d-7541e11d09ba
title: Simple Design to Achieve Red-to-Near-infrared Emissive Cationic Ir(III) Emitters and their use in Light Emitting Electrochemical Cells (dataset)
DOI: 10.17630/b85f7d91-8c56-4f8c-9a94-70a498ea8f11
title: Data undepinning: Analysis of the intrinsically disordered N-terminus of the DNA junction-resolving enzyme T7 endonuclease I

description: CW Experimental files in Bruker format that can be opened with Xepr (bruker software) or easyspin.
PELDOR experimental files that can be opened with Xepr or DeerAnalysis software
DOI: 10.17630/12bb1529-4947-4d75-b607-583606a66652
title: Isothiourea-catalyzed enantioselective addition of 4-nitrophenyl esters to iminium ions (dataset)
DOI: 10.17630/421487e5-d537-4fc8-b067-b6dd664c20d3
title: Data underpinning "Aryloxide-Facilitated Catalyst Turnover in Enantioselective α,β-Unsaturated Acyl Ammonium Catalysis"
DOI: 10.17630/3875a099-bb75-4ae1-82e5-0b98b6b7ebc6
title: Ormosil-Coated Conjugated Polymers for the Detection of Explosives in Aqueous Environments (dataset)
DOI: 10.17630/c320ef68-9292-497c-aeaa-2a80c1d1efc8
title: Grey seal stable carbon and nitrogen values from canine teeth and associated prey data

description: d13C and d15N values of collagen extracted from grey seal canine teeth (Table A1), and values of white muscle tissue from potential grey seal fish prey species collected in the North Sea in 2009. To accompany manuscript: 'Multi-decadal and ontogenetic trophic shifts inferred from stable isotope ratios of pinniped teeth' published in OIKOS
DOI: 10.17630/7ec84b4b-d2ab-493c-aaf6-5503a44c0eb5
title: Barrierless Slow Dissociation of Photogenerated Charge Pairs in High-Performance Polymer-Fullerene Solar Cells (dataset)
DOI: 10.6084/m9.figshare.25027646
title: quantum-confined-stark-effect-in-quantum-dots-data

description: This repository contains the data associated with the research paper [1].[1] Joe Mahoney, Mingchu Tang, Huiyun Liu, and Nicolás Abadía, "Measurement of the quantum-confined Stark effect in InAs/In(Ga)As quantum dots with p-doped quantum dot barriers," Opt. Express 30, 17730-17738 (2022). DOI: 10.1364/OE.455491.
DOI: 10.17631/rd-2024-0001-ddat
title: Dataset of a psychometric evaluation of Professional Quality of Life Scale Version 5 (ProQOl 5)

description: The current dataset includes responses from 366 UK-based mental health professionals (clinical psychologists, counsellors, and psychotherapists) for a psychometric study to examine the internal structure of Professional Quality of Life Scale Version 5 (ProQOL 5; Stamm, 2010). In the submitted version, responses to five items - 1, 4, 15, 17, and 29 - were reverse-scored as per the scale manual, and missing values were imputed. The Excel document contains two sheets: (i) the first sheet, titled, "ProQOL 5 Data" contains participants' responses to 30 ProQOL 5 items, in addition to demographic items, and; (ii) the second file, titled, "Demographic key" includes supporting information for deciphering the coding of demographic variables (e.g., '1' represents males and '2' represents females). For convenience, in the Excel sheet, "ProQOL 5 Data", the first row includes headings, and the scale items are prefixed with abbreviations of subscales (e.g., BO1 represents "item 1", which is a part of the Burnout (BO) subscale).
DOI: 10.34740/kaggle/dsv/7435987
title: MoviesDatasets
DOI: 10.3886/e182668
title: Data and code for: Predicting Cooperation with Learning Models

description: The data is compiled from 17 different experimental papers on the infinitely repeated prisoner's dilemma game. <br><br>Abstract: We use simulations of a simple learning model to predict cooperation rates in the experimental play of the indefinitely repeated prisoner’s dilemma. We suppose that learning and the game parameters only influence play in the initial round of each supergame, and that after these rounds play depends only on the outcome of the previous round. We find that our model predicts out-of-sample cooperation at least as well as models with more parameters and harder-to-interpret machine learning algorithms. Our results let us predict the effect of session length and help explain past findings on the role of strategic uncertainty.
DOI: 10.3886/e183481
title: Code for Exit Dilemma: The Role of Private Learning on Firm Survival

description: These files generate figures 1-7.
DOI: 10.34740/kaggle/dsv/7435992
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5066/p9b0s8l0
title: Data and scripts used in water-quality characteristics of the Red River of the North and tributaries in the Fargo-Moorhead metropolitan area, North Dakota 2019-2022

description: This dataset contains data in support of a study by the U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers, to describe water-quality characteristics of the Red River of the North and tributaries prior to construction of the Flood Risk Management Project in the Fargo-Moorhead metropolitan area. This dataset contains bacteria data for ten sites supplied by the North Dakota Department of Environmental Quality, pesticide data for five sites supplied by the North Dakota Department of Agriculture, results from quality control samples collected at ten sites by the USGS as well as files necessary to run loads for ten sites and seven constituents published in U.S. Geological Scientific Investigations Report 2023-5136 [Galloway, J.G., Nustad, R.A., and Wheeling, S.L., 2023, Water-Quality Characteristics of the Red River of the North and Tributaries in the Fargo-Moorhead metropolitan area, North Dakota 2019-2022: U.S. Geological Survey Scientific Investigations Report 2023-5136]. For additional information see the metadata file specific to each dataset. The readme.txt file describes how to run the loads and water-quality and streamflow data used for computing loads is provided in this data release, but are available from the USGS National Water Information System.