Resultados de investigación

Dataset on accuracies of the working review paper on costs and accuracies associated with using proximal soil sensing for high-resolution soil mapping. The RMSE data and related information were extracted from around 200 articles (references are included). This dataset is associated with the following DOI: https://doi.org/10.5281/zenodo.14035520, European Commission: EJP SOIL - Towards climate-smart sustainable management of agricultural soils 862695, Peer reviewed

Nutrient concentrations (NO3−, NO2−, NH4+, PO43−, and Si(OH)4), water oxygen isotope compositions (δ18O), and salinity were measured for ice samples from melt pond, and water from melt pond and lead during MOSAiC Leg 5 (August and September 2020). Ice cores for the melt pond bottom ice and melt pond surface ice were collected using an ice corer (Mark II coring system, Kovacs Enterprises, Inc., Indianapolis, USA). Also, we sampled small pieces of the pond bottom ice dislodged and floated to the surface of the melt pond by poking with a ruler. After sampling ice samples, we immediately drilled holes on ice and measured ice temperatures with a needle-type temperature sensor (Testo 110 NTC, Brandt Instruments, Inc., USA). Ice cores were segmented at 10-cm intervals. Dislodged ice was cut into 0.25 m×0.25 m size. All ice samples were placed into ice melting bags (Smart bags PA, AAK 5L, GL Sciences Inc., Japan) and melted in the dark at +4°C. For the discrete water sampling from the melt ponds and leads, first, we checked the vertical structure and depth of the meltwater layer from the same hole used for the RINKO Profiler (ASTD152, JFE Advantech, Japan) by attaching a conductivity sensor (Cond 315i, WTW GmbH, Germany) to a 2-m-long ruler and inserting the ruler into the lead water until the salinity measured with the Cond 315i increased at the meltwater–seawater interface. Water was pumped up with a peristaltic pump through a 2-m-long PTFE tube (L/S Pump Tubing, Masterflex, USA) at depths corresponding to meltwater (surface), the interface between meltwater and seawater (interface), and seawater (bottom). Salinity was measured at each depth by attaching a Cond 315i conductivity sensor to the bottom of the ruler. The tube intake was likewise attached to the bottom of the ruler. Ice melt water and water samples from melt pond and lead were subsampled into a 30-mL glass, screw-cap vials for later δ18O analysis, a 100-mL polypropylene bottles for salinity, and into 50-mL polyethylene screw-cap vials (Thermo Fisher Scientific Inc., Waltham, MA, USA) for later measurement of nutrient concentrations, after filtration through a 0.22-μm Durapore polyvinylidene fluoride membrane filter (MILLEX GV Filter unit, Merck Millipore Ltd., Germany). Samples for δ18O were stored at room temperature (+20°C). Salinity was measured with the same conductivity sensor used for the sea ice (Cond 315i, WTW GmbH, Germany). The samples for nutrient measurements were stored in a freezer (–20°C) before analysis. Oxygen isotope analyses were carried out with a mass spectrometer (DELTA-S Finnigan MAT, USA) at the ISOLAB Facility at AWI Potsdam by the equilibration method (details in Meyer et al., 2000; 2022). Nutrient samples were analyzed at the AWI Nutrient Facility between January and March 2021 using a Seal Analytical AA3 continuous flow auto-analyzer, controlled by AACE software version 7.09. Best-practice procedures for the measurement of nutrients were adopted following GO-SHIP recommendations (Becker et al., 2019; Hydes et al., 2010)., Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven (AWI), grant/award no. AFMOSAiC-1_00: Multidisciplinary drifting Observatory for the Study of Arctic Climate; Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven (AWI), grant/award no. AWI_PS122_00: Multidisciplinary drifting Observatory for the Study of Arctic Climate / MOSAiC, Peer reviewed

Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

Consejo Superior de Investigaciones Cientificas (CSIC), Peer reviewed

Sota el marc del projecte Estudi i Recuperació de Patrimoni Musical, aquest dataset conté dos arxius: Transcripció del manuscrit original de Joseph Picañol Enregistrament de la peça interpretada per: - Eva del Campo, orgue positiu. Orgue positiu construït per Gerrit Klop el 2022. - Ricart Renart, violí I. Violí construït per Joseph Massaguer el 1753. - Marta Guillen, violí II. Violí construït per Giuseppe Tenneriello el 2009. - Guillermo Turina, violoncel. Violoncel construït per Marchal a finals del segle XVIII. - Soledad Cardoso (soprano), Peer reviewed

Sota el marc del projecte Estudi i Recuperació de Patrimoni Musical, aquest dataset conté dos arxius: Transcripció del manuscrit original de Vocem meam Enregistrament de la peça interpretada per: - Eva del Campo, orgue positiu. Orgue positiu construït per Gerrit Klop el 2022. - Ricart Renart, violí I. Violí construït per Joseph Massaguer el 1753. - Marta Guillen, violí II. Violí construït per Giuseppe Tenneriello el 2009. - Guillermo Turina, violoncel. Violoncel construït per Marchal a finals del segle XVIII. - Martí Doñate (tenor), Peer reviewed

Sota el marc del projecte Estudi i Recuperació de Patrimoni Musical, aquest dataset conté dos arxius: Transcripció del manuscrit original de Francesc Queralt Enregistrament de la peça interpretada per: - Eva del Campo, orgue positiu. Orgue positiu construït per Gerrit Klop el 2022 - Ricart Renart, violí I. Violí construït per Joseph Massaguer el 1753 - Marta Guillen, violí II. Violí construït per Giuseppe Tenneriello el 2009 - Guillermo Turina, violoncel. Violoncel construït per Marchal a finals del segle XVIII - Martí Doñate (tenor), Peer reviewed

Sota el marc del projecte Estudi i Recuperació de Patrimoni Musical, aquest dataset conté dos arxius: Transcripció del manuscrit original de Joaquim Laposaria Alsina Enregistrament de la peça interpretada per: - Dani Espasa, piano de taula Miguel Slocker de 1831, cedit pel Museu de la Música de Barcelona., Peer reviewed

These timecourses and datasets correspond to the publication: Lokanathan-Balaji et al. “DNA binding and bridging by human CtIP in the healthy and diseased states” bioRxiv 2023 (https://doi.org/10.1101/2023.12.14.571649), Fig4+5_Datasets.zip and Fig4+5_Timecourses.zip folders contain all the datasets and the individual timecourses, respectively, for all the conditions addressed in Figures 4 and 5: - Bare DNA - DNA + wild-type CtIP - DNA + lambda CtIP - DNA + R100W CtIP - DNA + deltaC608 CtIP - DNA + deltaC782 CtIP - DNA + deltaN500 CtIP - DNA + deltaN790 CtIP Supp_FigS4_Datasets.zip and Supp_FigS4_Timecourses.zip folders contain all the datasets and the individual timecourses, respectively, for all the conditions addressed in Supplementary Figure S4: - fork-end branched DNA (forked) + wild-type CtIP - fork-end branched DNA (forked) + lambda CtIP, El dataset se puede consultar y descargar en https://doi.org/10.5281/zenodo.10499058, Peer reviewed

The data is stored in python compressed array format (.npz) with the atomization energies in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains five numpy arrays import numpy as np data = np.load('P+H_ThirdF.npz') data['R'] # Cartesian coordinates of nuclei (Ang.) data['E'] # Total energy (kcal/mol) data['F'] # Atomic forces (kcal/mol/Ang.) data['N'] # Number of atoms in each structure data['Z'] # Nuclear charges, This dataset was employed in the study of the reaction dynamics of H atoms reacting with P atoms in amorphous solid water. The protocol for the generation of the dataset is highlighted in the publication "Reaction dynamics on amorphous solid water surfaces using interatomic machine-learned potentials", Peer reviewed