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Underlying data for Slimani et al. Identification of dominant hydrogeochemical processes for groundwaters in the Algerian Sahara supported by inverse modeling of chemical and isotopic data
<p>The data hereafter underlie the paper by Slimani et al. doi:10.5194/hess-20-1-2016,</p> <p>appeared to Hydrol. Earth Syst. Sci., 20, 1-23, 2016.</p> <p> </p> <p> </p> <p>1. File Tableaux_data.xls</p> <p> </p> <p>This is an Excel sheet file. It contains:</p> <p>- raw analytical data, mostly in mg/L;</p> <p>- data converted in mmol/L;</p> <p>- data corrected from the defect of cations - anions balance; the correction is made proportionally.</p> <p>- the previous data completed with logarithms of activities, computed by Phreeqc, </p> <p>for calcium, sulfate, carbonate and water; those data are used to plot equilibrium diagrams for calcite and gypsum (figure 6);</p> <p>- for Phreatic aquifer only, saturation indexes for halite, anhydrite, calcite, dolomite and gypsum, along with distance from south to north, used in figure 7.</p> <p>2. Directory Phreeqc_res</p> <p>Contains the input file with all samples from CI, CT and Phr in a single file, and the selected output file.</p> <p>All calculations were made with version phreeqc-3.1.2 and database sit.dat.</p> <p>3. Directory Inverse models</p> <p>This directory contains inverse models for computing transformations:</p> <p>- from CI (average) to CT (average):</p> <p>- from CT (average) to Phr (pole I, average);</p> <p>- from pure water to Phr (pole II, sample P036);</p> <p>- for mixing Phr pole I and II, and try to explain a sample typical of medium mixing ratio, sample P068.</p>
Dataset supporting "Reach-relevant somatosensory signals modulate tactile suppression"
<p>Here we provide the psychophysical and kinematic data reported in: Gertz, H., Voudouris, D., & Fiehler, K. (2017). Reach-relevant somatosensory signals modulate tactile suppression. <em>Journal of Neurophysiology</em>, jn.00052.2017. http://doi.org/10.1152/jn.00052.2017</p> <p>Tactile stimuli on moving limbs are typically attenuated during reach planning and execution. This phenomenon has been related to internal forward models that predict the sensory consequences of a movement. Tactile suppression is considered to occur due to a match between the actual and predicted sensory consequences of a movement, which might free capacities to process novel or task-relevant sensory signals. Here we examined whether and how tactile suppression depends on the relevance of somatosensory information for reaching. Participants reached with their left or right index finger to the unseen index finger of their other hand (body target) or an unseen pad on a screen (external target). In the body target condition, somatosensory signals from the static hand were available for localizing the reach target. Vibrotactile stimuli were presented on the moving index finger before or during reaching, or in a separate no-movement baseline block, and participants indicated whether they detected a stimulus. As expected, detection thresholds before or during reaching were higher compared to baseline. Tactile suppression was also stronger for reaches to body targets than external targets, as reflected by higher detection thresholds and lower precision of detectability. Moreover, detection thresholds were higher when reaching with the left than with the right hand. Our results suggest that tactile suppression is modulated by position signals from the target limb that are required to successfully reach to the own body. Moreover, limb dominance seems to affect tactile suppression presumably due to disparate uncertainty of feedback signals from the moving limb.</p>
Dataset supporting Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants
<p>Executable benchmarks and result data set for the experimental evaluation in our publication "Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants" published at Hybrid Systems: Computation and Control (HSCC) 2017.</p> <p> </p> <p>Abstract:</p> <p>Modern control is implemented with digital microcontrollers, embedded within a dynamical plant that represents physical components.</p> <p>We present a new algorithm based on counter-example guided inductive synthesis that automates the design of digital controllers that are correct by construction. The synthesis result is sound with respect to the complete range of approximations, including time discretization, quantization effects, and finite-precision arithmetic and its rounding errors.</p> <p>We have implemented our new algorithm in a tool called DSSynth, and are able to automatically generate stable controllers for a set of intricate plant amodels taken from the literature within minutes.</p>
Research data supporting "Localized and controlled delivery of nitric oxide to the conventional outflow pathway via enzyme biocatalysis: towards therapy for glaucoma"
<p>Research data supporting the paper above published at DOI: 10.1002/adma.201604932</p>
Dataset supporting "Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli."
<p>Here we provide fMRI data used for the following project (for details see data description file): Gertz, H., Hilger, M., *Hegele, M., & *Fiehler, K. (2016). Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli. Neuroimage, doi: 10.1016/j.neuroimage.2016.05.043. (*shared last authorship)</p> <p> </p> <p>Previous studies have shown that beliefs about the human origin of a stimulus are capable of modulating the coupling of perception and action. Such beliefs can be based on top-down recognition of the identity of an actor or bottom-up observation of the behavior of the stimulus. Instructed human agency has been shown to lead to superior tracking performance of a moving dot as compared to instructed computer agency, especially when the dot followed a biological velocity profile and thus matched the predicted movement, whereas a violation of instructed human agency by a nonbiological dot motion impaired oculomotor tracking (Zwickel et al., 2012). This suggests that the instructed agency biases the selection of predictive models on the movement trajectory of the dot motion. The aim of the present fMRI study was to examine the neural correlates of top-down and bottom-up modulations of perception–action couplings by manipulating the instructed agency (human action vs. computer-generated action) and the observable behavior of the stimulus (biological vs. nonbiological velocity profile). To this end, participants performed an oculomotor tracking task in an MRI environment. Oculomotor tracking activated areas of the eye movement network. A right-hemisphere occipito-temporal cluster comprising the motion-sensitive area V5 showed a preference for the biological as compared to the nonbiological velocity profile.Importantly,a mismatch between instructed human agency and a nonbiological velocity profile primarily activated medial-frontal areas comprising the frontal pole, the paracingulate gyrus, and the anterior cingulate gyrus, as well as the cerebellum and the supplementary eye field as part of the eye movement network. This mismatch effect was specific to the instructed human agency and did not occur in conditions with a mismatch between instructed computer agency and a biological velocity profile. Our results support the hypothesis that humans activate a specific predictive model for biological movements based on their own motor expertise. A violation of this predictive model causes costs as the movement needs to be corrected in accordance with incoming (nonbiological) sensory information.</p>
Supporting data for "Synthesizing long-term sea level rise projections - the MAGICC sea level model v2.0"
<p>This is supporting data and configuration information to reproduce results from the MAGICC sea level model (DOI: 10.5281/zenodo.572395) presented in Nauels et al. (2017), using version 7.0 beta of the simple climate carbon-cycle model MAGICC (Meinshausen et al. 2011). For a compiled or source code version of MAGICC including the sea level model (git hash: c5c4e05518ed99f2bd53e2c6e68d238bbd6f17ec), please contact alexander.nauels@climate-energy-college.org.</p> <p>MAGICC input data and CMIP5 reference datasets are provided as a zip-file.</p> <p><br> REFERENCE DATASETS</p> <p>For MAGICC version 7.0 beta, the ocean model has been updated to emulate CMIP5 ocean temperatures and thermal expansion. The calibration results shown in Nauels et al. (2017) are based on potential ocean temperature (thetao) and thermal expansion (zostoga) reference datasets that are provided the 'data' directory. Reference datasets for the other sea level components have to be requested from the authors of the corresponding studies (Marzeion et al. 2014, Fettweis et al. 2013, Nick et al. 2013, Ligtenberg 2013, Levermann 2014). All relevant CMIP5 MAGICC input (.IN) is provided in the 'run' directory.</p> <p><br> MAGICC MODEL CONFIGURATION</p> <p>To customize a MAGICC run, namelist entries have to be modified in the configuration file 'MAGCFG_USER.CFG'. In order to select a CMIP5 model specific MAGICC ocean calibration, the model setup has to be called with the 'FILE_TUNINGMODEL_XX' entry, e.g. 'OCNTUNE_CCSM4' for the CCSM4 model. Automatically, the corresponding initial ocean temperature profile and model specific ocean layer area fractions will be applied. For prescribing the respective surface air temperatures, the namelist flag 'CORE_PRESCRTEMP_APPLY' has to be set to 1, with the model specific temperature dataset defined by 'FILE_PRESCR_SURFACETEMP', e.g. 'CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN'. The following MAGICC namelist entries have to be adapted in order to fully reproduce CMIP5 consistent results presented in Nauels et al. (2017): </p> <p>[...]<br> e.g. FILE_TUNINGMODEL_1 = "OCNTUNE_CCSM4",<br> [...]<br> CORE_SWITCH_TEMPADJUST_OCN2ATM = 1,<br> CORE_SWITCH_OCN_TEMPPROFILE = 2,<br> CORE_SWITCH_OCN_AREAFACTOR = 1,<br> CORE_PRESCRTEMP_APPLY = 1,<br> e.g. FILE_PRESCR_SURFACETEMP = "CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN",<br> [...]<br> OUT_TEMPERATURE = 1,<br> OUT_TEMPOCEANLAYERS = 1,<br> OUT_SEALEVEL = 1,<br> OUT_PARAMETERS = 1,<br> [...]<br> OUT_ASCII_BINARY = "ASCII",<br> [...]</p> <p>MAGICC output is stored in the 'out' directory. Depending on the flag 'OUT_ASCII_BINARY', either ASCII or BINARY files are produced for the output parameters which are set to 1 in the namelist, e.g. 'OUT_SEALEVEL = 1'. </p> <p><br> MAGICC SEA LEVEL MODEL LICENSE</p> <p>This source code of the MAGICC sea level model is distributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/legalcode).</p> <p><br> MAGICC PARENT MODEL LICENSES</p> <p>The MAGICC executable is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license (https://creativecommons.org/licenses/by-nc-sa/3.0/). The MAGICC source code is available under a separate license agreement. Any derivatives have to be fed back to the MAGICC developers, so that users of future MAGICC versions can have the benefit of applying the model alterations, enhancements etc. Furthermore, we would like you to provide feedback, bug reports and development suggestions.</p> <p><br> REFERENCES</p> <p>Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, 2013.</p> <p>Levermann, A.,Winkelmann, R., Nowicki, S., Fastook, J. L., Frieler, K., Greve, R., Hellmer, H. H., Martin, M. A., Meinshausen, M., Mengel, M., Payne, A. J., Pollard, D., Sato, T., Timmermann, R., Wang, W. L., and Bindschadler, R. A.: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models, Earth Syst. Dynam., 5, 271–293, 2014.</p> <p>Ligtenberg, S. R. M., van de Berg, W. J., van den Broeke, M. R., Rae, J. G. L., and van Meijgaard, E.: Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model, Climate Dynamics, 41, 867–884, 2013.</p> <p>Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration, Atmospheric Chemistry and Physics, 11, 1417–1456, 2011.</p> <p>Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K., and Wigley, T. M. L.: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0, Geosci. Model Dev., 2017.</p> <p>Nick, F. M., Vieli, A., Andersen, M. L., Joughin, I., Payne, A., Edwards, T. L., Pattyn, F., and van de Wal, R. S. W.: Future sea-level rise from Greenland’s main outlet glaciers in a warming climate, Nature, 497, 235–238, 2013.</p>
Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries (Supporting Data)
<p>This data accompanies the paper "Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries", published in Journal of Computational Physics (2017), http://dx.doi.org/10.1016/j.jcp.2017.06.009.</p>
Causes and importance of new particle formation in the present-day and pre-industrial atmospheres: supporting data
<p>Data presented in the manuscript "Causes and importance of new particle formation in the present-day and pre-industrial atmospheres" currently in review.</p> <p>Particle number concentrations (files with initial word "CCN" or "N3") have units of particles per cubic centimetre, calculated at ambient temperature and pressure. Files with initial word "solar" have units of percent. Ion production rates have units ion pairs per cubic centimetre per second.</p> <p>The simulation data presented here was generated with the GLOMAP aerosol model, https://www.see.leeds.ac.uk/research/icas/research-themes/atmospheric-chemistry-and-aerosols/groups/aerosols-and-climate/the-glomap-model/ running on a T42 grid.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p> <p> </p> <p> </p>
Short rotation woody crop decision support system
<p>From http://edis.ifas.ufl.edu/fr169</p> <p>Plantations of short-rotation woody crops (SRWCs) use fast-growing tree species that coppice, i.e., resprout from the stump, for repeated harvests that minimize planting costs. Under coppice management, 3–5 growth stages (coppices) can be harvested during the SWRC life (rotation or cycle), with each coppice lasting 2–10 years. SRWCs can produce wood for biomass, mulch, pulpwood, and other products, while also providing environmental services. For example, SRWC plantations can be irrigated with municipal wastewater or fertilized with treated biosolids or municipal compost, simultaneously increasing biomass production, reducing fertilizer costs, and intercepting nitrates and phosphates to reduce nutrient loading in waterways (Rosenqvist et al. 1997; Labrecque et al. 1997; Aronsson & Perttu 2001; Rockwood et al. 2004; Licht & Isebrands 2005; Langholtz et al. 2005; Mirck et al. 2005). SRWCs can also help build soil organic matter, recycle nutrients, and maintain vegetative cover to restore ecological functions of mined lands and other degraded lands (Stricker et al. 1993; Bungart & Huttl 2001; Rockwood et al. 2006). SRWCs established on agricultural lands as shelterbelts or buffer zones to protect riparian areas are likely to reduce soil erosion and runoff of agricultural inputs and improve wildlife habitat (Joslin & Schoenholtz 1997; Tolbert & Wright 1998; Thornton et al. 1998). In spite of these benefits, SRWC production is not always economically viable, and evaluating the economics of SRWC production is not easy.</p> <p>Because SRWCs can have multiple coppices per rotation, evaluating the economics of SRWCs is more complicated than that of conventional forestry. For example, in the evaluation of a pine plantation, the future value of harvested timber is discounted to the year of planting, and planting costs are subtracted to calculate the net present value (NPV) of one harvest rotation. NPV is then used to calculate land expectation value (LEV), i.e. the value of the land assuming the adoption of this forestry practice. However, in the case of SRWC systems, multiple coppices require that the value of every coppice is discounted to the beginning of the rotation. Furthermore, the costs associated with establishment of each rotation and coppice stage must be discounted differently, and determining the optimum harvest scheduling and replanting age is also more complicated than for conventional forestry. Theory behind economic evaluation and optimization of SRWCs is described by Medema & Lyon (1985), Tait (1986), and Smart & Burgess (2000). Economics of SRWC systems in Florida are evaluated by Langholtz et al.<em> </em>(2005; 2007).</p> <p> </p> <p>The Florida Institute of Phosphate Research (FIPR) has supported research in the development of SRWCs as commercial tree crops on phosphate mined lands in Florida. A product of this research is a SRWC Decision Support System (DSS) that can be used to evaluate the economic viability of SRWC systems. The DSS allows a user to input operational costs, planting densities, stumpage prices and other variables and calculate NPVs, LEV, equal annual equivalent (EAE), internal rate of return (IRR), and benefit/cost ratio of a SRWC system. The DSS is in the form of a Microsoft® Excel spreadsheet (Figure 1).</p> <p>The DSS allows users to enter variables in yellow cells in the “Inputs” section on the left side of the worksheet and view results in green cells in the “Outputs” section on the right. Input variables include stumpage price, capital cost, and costs of each start-up, rotation, coppice, and year. The user can specify what portion of total biomass is harvested, the number of coppices, and their harvest ages. Financial incentives for renewable energy or other environmental benefits can be incorporated on a per-ton basis in the stumpage price. The DSS uses growth and yield functions developed from measurements of two planting densities of <em>Eucalyptus amplifolia</em> in a field trial of SRWCs on a phosphate mine clay settling area (CSA) near Lakeland, FL. Yields for each growth stage are displayed, and can be modified by adjusting the initial planting density or by adjusting yields under the general parameters. Ranges of values used to assess SRWC production on CSAs are shown in Table 1.</p> <p>Under all possible combinations of the assumptions in Table 1, the profitability of <em>E. amplifolia</em> on CSAs varies widely, with LEVs ranging from -$909 to $6,740 acre<sup>-1</sup>. Under the base case scenario identified in Table 1, the resulting LEV is $308 acre<sup>-1</sup> assuming an interest rate of 10% and $2,633 acre<sup>-1</sup> assuming an interest rate of 4%. LEV, EAE, and IRR results of the base case scenario under a range of discount rates and stumpage prices are shown in Table 2.</p> <p>This DSS does not automatically determine optimum harvest ages or the optimum number of stages per cycle, which both require dual optimization of continuous functions. DSS users can either input probable harvest and replanting ages and “zero in” inputs to maximize economic returns, or contact the authors to arrange a customized DSS. The DSS in either Excel or MathCad format could be modified to incorporate alternative growth and yield functions that might be developed for other SRWC species or conditions. For more information see the FIPR report “Commercial Tree Crops for Phosphate Mined Lands”, Rockwood et al. (in press).</p> <p> </p> <p>From http://edis.ifas.ufl.edu/fr169</p>
Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"
<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>
Data Supporting: Identification of Nonlinearity Sources in a Flexible Wing
Open the record for dataset details and reuse information.
Supporting data for Shi et al. "Geospace Concussion: Global reversal of ionospheric vertical plasma drift in response to a sudden commencement"
<p>This dataset contains the simulation data supporting the paper titled "Geospace Concussion: Global reversal of ionospheric vertical plasma drift in response to a sudden commencement", submitted by Shi et al., 2022.</p>
Supporting data for Lin et al. "Diffuse Electron Precipitation Effects on Subauroral Polarization Streams"
<p>This dataset contains the necessary data supporting the paper titled "The role of diffuse electron precipitation in the formation of subauroral polarization streams", submitted by Lin et al., 2021. The dataset includes 1. the solar wind/IMF and SYMH indices used to drive the Multiscale Atmosphere-Geospace Environment (MAGE) model; 2. Electron precipitation energy flux (EnFlux), cross track ion drift, field-aligned current (FAC), and Pedersen conductance (SigmaP) sampled along three trajectories of DMSP F18 satellite during 17 March 2013 (08:28-08:38 UT, 11:18-11:28 UT, 13:00-13:10 UT), which are used to compare with the DMSP measurements; 3. Plasma pressure distribution in the magnetospheric equatorial plane, and EnFlux, FAC, westward ion drifts, at 06 UT, 07 UT, 09 UT, and 11 UT from the baseline run of MAGE simulation; 4. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from two MAGE simulation runs, one being the same from above mentioned, the other being a controlled run in which diffuse electron precipitation was turned off; 5. Zonal ion drifts, magnetospheric equatorial plane plasma density, and boundary locations of the EnFlux and downward FAC in both the ionosphere and magnetosphere; 6. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from another two MAGE simulation runs, one using the Fedder precipitation model, the other using the RCM+Zhang precipitation model with a loss rate of one-tenth.</p>
Data and code in support of "Quantifying international climate finance needs and its social and climate benefits"
<div> <div>These folders contain the code and figures for experiments, data analysis and visualization of results associated with the paper "Quantifying international climate finance needs and its social and climate benefits".</div> <br> <div>The folder FullAdaptation contains the data needed to produce the figures reported in the main text assuming adaptation as in Agrawala et al.</div> <div>The folder WITCHAdaptation contains the data needed to produce the figures reported in the supplementary material and discussed in the context of alternative assumptions for adaptation modeling.</div> <br> <div>Each of these folders contains:</div> <div>- the original optimization experiments (with and without transfers under different inequality aversion levels);</div> <div>- the CRICE code which is used to simulate the obtained policies from the folders above;</div> <div>- folders containing figures (main/supp figures and optimization runtimes);</div> <div>- world-administrative boundaries shapefile (https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries/export/);</div> <div>- script to compile the optimization version of CRICE (compile_run.sh). This is needed to run the optimizations but it requires substantial computational time;</div> <div>- script to select the best solution/runtime across seeds (best_runtime.py);</div> <div>- script to simulate the best solutions (simulate_sensitivity_mitigation.py). Before running this run "make clean && make" in the CRICE50 folder to make sure the simulation model is compiled;</div> <div>- script to produce figures (analyze_results.py, plot_figures.py, plot_maps.py, plot_runtime.py).</div> </div>
Data supporting "Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code"
<p>The data consists of a CSV file containing the raw performance data collected from running our decoder on a Xilinx Virtex Ultrascale+ VU19P FPGA. The Stim circuits that were used to create the samples are provided in a ZIP file. Our internal fork of Stim with support for leakage is needed to sample noise from the circuits.</p>
Supporting datasets for Cisneros et al. 2024
<p>Supporting datasets for Cisneros et al., 2024. Mutational biases promote neutral increases in the complexity of protein interaction networks following gene duplication.</p>
A systems analysis of sustainability impacts of agricultural policies in India - R script and supporting files
<p>This includes : </p><ul><li>two R scripts used to implement the model (residue_burning. R) and generate results (residue_burning_calls.R)</li><li>three supporting .csv files used in the R scripts above </li></ul><p> </p>
[VERSION 2] Data set and analytic codes supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?"
<p>This is <strong>VERSION 2</strong> of data set and analytic codes (with a meta data [see the meta data from VERSION 1]) supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?". We investigated the impacts of replanting and alternative replanting decisions (replanting with monoculture versus polyculture oil palm plantations) on within-plantation environmental conditions and butterfly assemblages (diversity, density, and composition). We also assessed the effects of habitat structure and complexity within plantations on butterfly assemblages. Apart from "BantingButterflies_ButterflyData", other data are the same as in VERSION 1.</p><p><strong>## List of changes:</strong></p><p># 1. <i>Tirumala septentrionis </i>was not included in the analyses because it should have been <i>Ideopsis vulgaris</i> (had been corrected),</p><p># 2. PC5 and PC6 (from PCA) were considered as predictors for the GLMs,</p><p># 3. The Mantel test was added.</p><p><strong>## Other notes:</strong></p><p># 1. Older version of ggiNEXT could work with facet.var = "site", now it needs to be "Assemblage"</p><p># 2. Older version of ggiNEXT could work with facet.var = "order", now it needs to be "Order.q"</p><p># 3. "set.seed(42)" function was used before running "iNEXT", ANOSIM, and the Mantel test to get reproducible outputs (exactly the same outputs every time each function is run).</p><p><strong>Funding and research permission:</strong> Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund provided funding for MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) provided funding for the establishment of the plots and surveys of environmental parameters. Research permission was provided by the Economic Planning Unit (EPU) of Malaysia's Prime Minister's Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
Black American women's attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety
<p><strong>Objective</strong>: This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.</p> <p><strong>Methods</strong>: A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identify as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety.</p> <p><strong>Results</strong>: The findings of the study (N=395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased the odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the South region of the U.S. had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions.</p> <p><strong>Discussion</strong>: Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety.</p> <p><strong>Conclusion</strong>: Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase the likelihood of adoption of and engagement with digital mental health interventions for management of anxiety. </p>
Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor
<p>Summary of published carbon dioxide field emission data and their influence factors used for generating Tier 1 emission factor of peat extractions in IPCC 2013 Wetland Supplementary and extra data published after IPCC (2014). </p><p>The dataset is supplementary to the published paper "Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor" By Hongxing He and Nigel Roulet: He, H., Roulet, N.T. Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor. <i>Commun Earth Environ</i> <strong>4</strong>, 436 (2023). https://doi.org/10.1038/s43247-023-01091-y. </p><p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.