Find research datasets worth reusing

opencc-by-nd-4.0Jan 2023View details →

Figure 16 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines

Figure 16. Responses to the jumping spider, high detail text prompt by the Replicate Stable Diffusion engine (#7).

opencc-by-nd-4.0Jan 2023View details →

Figure 22 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines

Figure 22. Responses to the jumping spider, high detail text prompt by the OpenAI DALL-E 2 engine (#10).

DGFI-TUM DSO1 orbits of altimetry satellites TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 derived from SLR data in the SLRF2014 reference frame

The data set provides DSO1 orbits of altimetry satellites TOPEX/Poseidon (27 September 1992 to 9 October 2005), Jason-1 (13 January 2002 to 30 June 2013), Jason-2 (20 July 2008 to 2 October 2019) and Jason-3 (17 February 2016 to 24 October 2021) computed at the Deutsches Geodätisches Forschungsinstitut of the Technical University of Munich (DGFI-TUM). The orbits are derived using the DGFI-TUM Orbit and Geodetic parameter estimation Software (DOGS). The orbits were computed from SLR data in the SLRF2014 (an extended version of ITRF2014) reference frame using common for all satellites, most precise models and standards available and described in the following paper that serves as a citation of these orbits: Sergei Rudenko, Denise Dettmering, Julian Zeitlhöfler, Riva Alkahal, Dhruv Upadhyay and Mathis Bloßfeld (2023) Radial orbit errors of contemporary altimetry satellite orbits. Surveys in Geophysics, https://doi.org/10.1007/s10712-022-09758-5. For each satellite, a tar file is given comprising compressed files. File names are given as satgpswd.sp3.gz, where “sat” is the abbreviation of the satellite name (JA1, JA2, JA3, TPX), “gpsw” is the 4-digit GPS week, and “d” indicates the day of the GPS week containing the first time instant of the file (0 = Sunday, 6 = Saturday). The orbit files are available in the Extended Standard Product 3 Orbit Format, Version c (SP3-c). The orbits were derived within the project “Mitigation of the current errors in precise orbit determination of altimetry satellites (MEPODAS)” funded by Deutsche Forschungsgemeinschaft (DFG).

Solution, Crystal and in-Silico Structures of the Organometallic Vitamin B12-Derivative Acetylcobalamin and of its Novel Rhodium-Analogue Acetylrhodibalamin

Cartesian coordinates of all calculated structures discussed in the paper.

Frozen feather and motif files for Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue.

Frozen feather and motif files for manuscript "Distinct stage-specific transcriptional states of B cells derived from human tonsillar tissue" by Espinoza DA, et al. Data was downloaded from cisTarget database from aertslab.org.

opencc-zeroFeb 2023View details →

dryad36/100

Patterns and determinants of plant-derived lignin phenols in coastal wetlands: implications for organic C accumulation

1. As a major plant-derived soil organic carbon (SOC) component, lignin phenols are unique biomarkers that reflect biogeochemical characteristics under different vegetation compositions and climatic zones in coastal wetlands. However, the latitudinal patterns of plant-derived lignin phenols to SOC and their link with the stability and controlling mechanisms remain poorly understood. 2. A total of 156 soil samples from 39 sites along a 5000 km coastal transect, were taken to explore the effects of biological and environmental controls on the patterns of lignin phenols. Lignin phenols had contents ranging from 1.91 to 83.3 mg g−1 OC, and a positive correlation was detected in grass-dominated salt marsh, but a weakly negative correlation in mangrove. Positive correlations between SOC or lignin content and C/V or S/V (the cinnamyl- or syringyl-to-vanillyl) ratios were found, while overall negative correlations between SOC or lignin content and (Ad/Al)V or (Ad/Al)S (the acid-to-aldehyde of vanillyl or syringyl units) ratios were detected, respectively, which confirmed the validity of these lignin biomarker degradation parameters. 3. Our findings revealed that plant C inputs and monomer ratios directly influenced the capacity of lignin phenols in soils. Lignin content and stabilization was mainly controlled by soil properties (i.e., pH, EC, sand/clay). Mean annual temperature (MAT) influenced the patterns of lignin phenols both directly by increasing decomposition and indirectly by changing the vegetation and soil biogeochemistry (i.e., microbial substrate availability). 4. Coastal wetlands are characterized by high primary productivity and C burial rate, yet plant-derived lignin phenols are not as much as we thought compared to microbial residues C. Precise identification and quantification of the origin, decomposition, and determinants of lignin phenols help us understand their contribution to C sequestration and its response to climate and environmental changes.

Prioritizing New Inhibitors for TPD1 from a Library of Anthraquinone and Chalcone Derivatives.

The main purpose of virtual screening is to rank order hit compounds according to their affinity to the protein target. Several functions and techniques have been developed to achieve this purpose; however, simple and forward methods were found less efficient than the complex techniques. In this study, we aim at establishing a workflow that reduces the shortfalls of single methods by integrating structure- and ligand-based screening, especially when an imbalanced dataset is presented. QSAR predictions, pharmacophoric mapping, and shape similarity add up to the docking scores that are all fused to form a parallel Z-score fusion of the scores. The top-ranked structures in the four screening techniques, in addition to the consensus and molecular mechanics (MMGBSA) rescoring methods were compared with co-crystalized ligands and TDP1 inhibitors in terms of their molecular dynamic simulations, binding free energy, and poses. Interestingly, consensus and MMGBSA rescoring methods prioritized the most chemically-diverse structures among other screening ways. Screening by docking has brought structures with binding modes similar to the co-crystalized ligands, whereas, MMGBSA, consensus, and docking resulted in a dynamic simulation similar to and/or better than the co-crystalized ligands. Besides, QSAR-selected structures showed similar binding modes to the commercial TDP1 inhibitors. Notably, a strong correlation was established between the inhibitory concentrations and marketed TDP1 inhibitors’ binding energy values, thus, the inhibitory potencies of the top-ranked structures in such methods are expected to be in the same molar range. In conclusion, consensus scoring can be taken as an adaptable mainstay virtual screening methodology.

Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice

Spleens from mice infected with Plasmodium chabaudi were processed and stained for localization of monocyte-derived dendritic cells (MODCs) by flow cytometry. The markers utilized were: Live/Dead, F4/80, CD11b, DCSign, MHCII, CD11c and CD3. After sorted out, MODCs were frozen in liquid nitrogen, followed by metabolite extraction, as recommended by The Metabolomics Facility at MD Anderson. Metabolites were extracted using ice-cold 0.1% Ammonium hydroxide in 80/20 (v/v) methanol/water. Extracts were centrifuged at 17,000 g for 5 min at 4°C, and supernatants were transferred to clean tubes, followed by evaporation to dryness under nitrogen. Dried extracts were reconstituted in deionized water, and 5 μL was injected for analysis by ion chromatography (IC)-MS. IC mobile phase A (MPA; weak) was water, and mobile phase B (MPB; strong) was water containing 100 mM KOH. A Thermo Scientific Dionex ICS-5000+ system included a Thermo IonPac AS11 column (4 µm particle size, 250 x 2 mm) with column compartment kept at 30°C. The autosampler tray was chilled to 4°C. The mobile phase flow rate was 350 µL/min and gradient from 1mM to 100mM KOH was used. The total run time was 60 min. To assist the desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative ionization mode at a resolution of 240,000.

Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- Derived data 2020 & 2021

This is derived data from a remote sensing experiment performed in 2020 and 2021. This repository contains .csv and .R files that can be used to replicate the analysis presented here: <a href="https://zenodo.org/record/6859791#.Y-K6ky-B1z8">https://zenodo.org/record/6859791#.Y-K6ky-B1z8</a> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov

Dataset related to article "MASSIVE IRON ACCUMULATION IN PKAN-DERIVED NEURONS AND ASTROCYTES: LIGHT ON THE HUMAN PATHOLOGICAL PHENOTYPE"

The database contains the microxygraphy data included in the article at title

Derivative products from "Spherical Harmonic Representation of Energetic Neutral Atom Flux Components Observed by IBEX" by Swaczyna, Dayeh, & Zirnstein

This dataset includes derivative data products obtained using the method described in: Swaczyna, Dayeh, & Zirnstein (2023), Spherical Harmonic Representation of Energetic Neutral Atom Flux Components Observed by IBEX The products have been derived from IBEX Data Release #16 (https://ibex.princeton.edu/DataRelease16).  <ul> <li>hvset_tabular_ram_cg.zip - results for ram-only Compton-Getting and survival probability corrected IBEX maps</li> <li>hvset_tabular_antiram_cg.zip - results for antiram-only Compton-Getting and survival probability corrected IBEX maps</li> </ul> Each archive includes the following files: <ul> <li>com_flux_yyyy_esa_e.txt - reconstructed flux map from spherical harmonic coefficients using the standard IBEX pixelization</li> <li>com_fvar_yyyy_esa_e.txt - reconstructed flux variance map from spherical harmonic coefficients using the standard IBEX pixelization</li> <li>com_ylm_coeff_yyyy_esa_e.txt - coefficients of the spherical harmonic representation</li> <li>com_ylm_mat_yyyy_esa_e.txt - covariance matrix providing uncertainties of the coefficients</li> <li>mask_ribbon_esa_e.txt - ribbon mask</li> <li>matrix_y.txt - matrix transforming the spherical harmonic coefficients into values in IBEX pixels</li> </ul> where: <ul> <li>com - indicate the included component of the ENA flux: <ul> <li>gdf - Globally DIstirbuted Flux</li> <li>rib - IBEX ribbon</li> <li>tot - both components (total maps)</li> </ul> </li> <li>e - enumerates IBEX energy steps (e = 2, 3, ... 6)</li> <li>yyyy - indicates map year (yyyy = 2009, 2010, ..., 2019 or 'single' for the time-combined map)</li> </ul>

Hadza Prevotella Require Diet-derived Microbiota Accessible Carbohydrates to Persist in mice.

Raw reads for whole genome sequencing and RNAseq for Gellman et al., 2023.

opencc-by-4.0Mar 2023View details →

Videos: The crystallization mechanism of gel-derived SiO2-TiO2 amorphous nanobeads elucidated by high-temperature in-situ experiments

Supplementary materials detailing high-temperature in-situ TEM experiments. Main article available at:  <a href="https://doi.org/10.1021/acs.cgd.3c00300">https://doi.org/10.1021/acs.cgd.3c00300</a>

opencc-by-4.0Mar 2023View details →

Dataset: Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering

Dataset for the results shown in the publications "Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering"

opencc-by-4.0Mar 2023View details →

Crypto Price Monitoring Dataset for On-chain Derivatives Research

# Crypto Price Monitoring Repository This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price. ## Data Files There are two CSV data files in this repository (one for each date): 1.  `Pricemon_results_2022_11_01.csv` 2.  `Pricemon_results_2022_11_08.csv` ## Data Format Both data files have the same format and structure, with the following five columns: 1.  `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH). 2.  `unified_symbol`: A standardized symbol used across different platforms. 3.  `time`: Timestamp for when the price data was recorded (in UTC format). 4.  `price`: The price of the crypto asset at the given time (in USD). 5.  `source`: The name of the price source for the data. ## Price Sources The `source` column in the data files refers to the provider of the price data for each record. The sources include: -   `chainlink`: Chainlink Price Oracle -   `mycellium`: Built-in oracle of the Mycellium platform -   `bitfinex`: Bitfinex cryptocurrency exchange -   `ftx`: FTX cryptocurrency exchange -   `binance`: Binance cryptocurrency exchange ## Usage You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool. ### Example Here's an example of how you can read and display the data using Python and the pandas library:        import pandas as pd                 # Read the data from CSV file        data = pd.read_csv('Pricemon_results_2022_11_01.csv')                 # Display the first 5 rows of the data        print(data.head())`    ## Acknowledgements These datasets were recorded and supported by <a href="https://datamint.ai">Datamint</a> company (value-added on-chain data provider) and its team. ## Contributing If you have any suggestions or find any issues with the data, please feel free to contact authors.

Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products

Data and Method described in https://www.mdpi.com/2072-4292/15/2/484, "Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products"

27 MAGs from the Family of Endozoicomonadaceae derived from Tara Pacific Metagenomes

This dataset contains 27 MAGs from the family of Endozoicomonadaceae generated from a subset of Tara Pacific metagenomes. Contextual information of the MAGs can be found in the associated publication:  Ecology of Endozoicomonadaceae in three coral species across the Pacific Ocean, Hochart et al, submitted

Measured pulse height spectra for five pieces of Chinese muSR detectors and their derivatives

This data set presents the measured pulse spectra of five pieces of Chinese muSR detectors which are made of plastic scintillators (size: 10*10*50 mm3). The derivatives and related plots are also listed in the file.

Evaluation of the pKa's of Quinazoline Derivatives : Usage of Quantum Mechanical Based Descriptors

In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the pKa’s of quinazoline derivatives, which constitute a very important class of natural and synthetic compounds in organic, pharmaceutical, agricultural and medicinal chemistry areas. Linear relationships between the experimental pKa’s and nine different DFT descriptors (atomic charge on nitrogen atoms (Q(N), ionization energy (I), electron affinity (A), chemical potential (m), hardness (h), electrophilicity index (w), fukui functions (f +, f -), condensed dual descriptor (Df) and local hypersoftness (s(2))) were considered. Several DFT methods (a combination of five DFT functionals and two basis sets) in conjunction with two different implicit solvent models were tested, and among them, M06L/6-311++G(d,p) level of theory employing the CPCM solvation model was found to give the strongest correlations between the DFT descriptors and the experimental pKa’s of the quinazoline derivatives. The calculated atomic charge on N1 atom (Q(N1)) was shown to be the best descriptor to reproduce the experimental pKa’s (R2=0.927), whereas strong correlations were also derived for A, w, m,  and Δf. In the last part, the applicability of isodesmic reaction scheme to the pKa prediction of quinazoline derivatives was tested, and the calculated A was shown to be a well-established method for the classification of molecules, and thus, for the identification of a suitable reference molecule for the calculations. The QM-based protocols presented in this study will enable fast and accurate high-throughput pKa predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for pKa predictions.