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Ecotoxicity test results for the paper entitled "Passive electrobioremediation approaches for enhancing hydrocarbons biodegradation in contaminated soils"
<p>Within the frame of the ELECTRA No. 826244 H2020 project, a microbial electrochemical snorkel (MES) was applied for the treatment of soil contaminated by hydrocarbons at a laboratory scale. The changes in soil ecotoxicity were followed by plant tests applying <em>Sinapis alba</em> and <em>Triticum aestivum</em>. The datasets (supplementary to Figure 7) for this publication are presented here. Referred publication: Carolina Cruz Viggi, Matteo Tucci, Marco Resitano, Bruna Matturro, Simona Crognale, Viktória Feigl, Mónika Molnár, Simona Rossetti, Federico Aulenta, Passive electrobioremediation approaches for enhancing hydrocarbons biodegradation in contaminated soils, Science of The Total Environment, 845, 157325, 2022, https://doi.org/10.1016/j.scitotenv.2022.157325.</p>
Impact of Passive Voice in Requirements Engineering
<p>This repository contains instrumentation material and results for the experiment described in the paper "On The Impact of Passive Voice Requirements on Domain Modelling" by Henning Femmer, Jan Kucera, and Antonio Vetrò from Technische Universität München.</p>
Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers"
<p>Data set for the replication package of the paper "Constriction of actin rings by passive crosslinkers".</p>
Total O3 columns at polar regions: TOMCAT/SLIMCAT passive and active tracers and merged SAOZ-MSR2 dataset
<p>Passive and active total ozone columns simulated by the chemical transport model TOMCAT/SLIMCAT (Chipperfield, 1999) and the merged dataset of total ozone from Système d'Analyse par Observation Zénithale (SAOZ, Pommereau and Goutail, 1988) ground-based instruments and Multi-Sensor Reanalysis (MSR2, van der A et al., 2010, 2015) for the polar stations described in the Table here below.</p> <p><strong>Table. Arctic and Antarctic stations included in the study: station name and ID, latitude, longitude and measurement periods of SAOZ and MSR2 datasets.</strong></p> <table> <tbody> <tr> <td> <p><strong>Station (ID)</strong></p> </td> <td> <p><strong>Lat, Lon</strong></p> </td> <td> <p><strong>SAOZ dataset</strong></p> </td> <td> <p><strong>MSR2 dataset </strong></p> </td> </tr> <tr> <td> <p>Eureka, Nunavut (EU)</p> </td> <td> <p>80.1°N, 86.4°W</p> </td> <td> <p>2005-2020</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Ny-Alesund, Svalbard (NY)</p> </td> <td> <p>78.9°N, 11.9° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Thule, Greenland (TH)</p> </td> <td> <p>76.5°N, 68.8°W</p> </td> <td> <p>1999-2003, 2005-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Scoresbysund, Greenland (SC)</p> </td> <td> <p>70.5°N, 22.0°W</p> </td> <td> <p>1991-2017, 2019-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sodankyla, Finland (SK)</p> </td> <td> <p>67.4°N, 26.6° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sondre Stromfjord, Greenland (SS)</p> </td> <td> <p>67.0°N, 50.6°W</p> </td> <td> <p>2018-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Zhigansk, Russia (ZH)</p> </td> <td> <p>66.8°N, 123.4° E</p> </td> <td> <p>1992-2013</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Salekhard, Russia (SA)</p> </td> <td> <p>66.5°N, 66.7°E</p> </td> <td> <p>2002-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Marambio, Antarctica (MB)</p> </td> <td> <p>64.2°S, 56.7°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Dumont d’Urville, Antarctica (DD)</p> </td> <td> <p>66.7°S, 140.0°E</p> </td> <td> <p>1989-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Rothera, Antarctica (RO)</p> </td> <td> <p>67.6°S, 68.1°W</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Syowa, Antarctica (SW)</p> </td> <td> <p>69.0°S, 39.6°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Neumayer, Antarctica (NM)</p> </td> <td> <p>70.7°S, 8.3°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Terra Nova, Antarctica (TN)</p> </td> <td> <p>74.8°S, 164.5°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Concordia, Antarctica (DO)</p> </td> <td> <p>75.1°S, 123.4°E</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Halley, Antarctica (HB)</p> </td> <td> <p>75.6°S, 26.8°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> </tbody> </table> <p> </p> <p>Each file corresponds to the whole winter time data set of the station identified by its ID: O3_PassActSLIMCAT_CompositeMSR2SAOZ_ID.txt</p> <p>1<sup>st</sup> column: Year</p> <p>2<sup>nd</sup> column: Day of Year (DoY)</p> <p>3<sup>rd</sup> column: Passive ozone column (without chemistry) modelled by TOMCAT/SLIMCAT (O3pasS).</p> <p>4<sup>th</sup> column: Active ozone column (with full chemistry) modelled by TOMCAT/SLIMCAT (O3actS).</p> <p>5<sup>th</sup> column: merged data from SAOZ observations and MSR2 (O3comp)</p> <p>Passive and active tracers of SLIMCAT were normalized to O3comp data in the beginning of the winter.</p> <p>NaN is used when there is no observation either from SAOZ instrument or MSR2 dataset or from the model.</p> <p> </p> <p><strong>References</strong></p> <p>Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, <a href="https://doi.org/10.1256/QJ.05.51">https://doi.org/10.1256/QJ.05.51</a>, 2006.</p> <p>Pommereau, J.-P., and Goutail, F.: O<sub>3</sub> and NO<sub>2</sub> ground-based measurements by visible spectrometry during arctic winter and spring 1988, Geophys. Res. Lett., 15, 891–894, <a href="https://doi.org/10.1029/GL015i008p00891">https://doi.org/10.1029/GL015i008p00891</a>,1988.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Multi sensor reanalysis of total ozone, Atmos. Chem. Phys., 10, 11277–11294, <a href="https://doi.org/10.5194/acp-10-11277-2010">https://doi.org/10.5194/acp-10-11277-2010</a>, 2010.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012, Atmos. Meas. Tech., 8, 3021–3035, <a href="https://doi.org/10.5194/amt-8-3021-2015">https://doi.org/10.5194/amt-8-3021-2015</a>, 2015.</p>
Station data of passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE-CalCOFI Augmented cruises in the California Current System, 2012 - October 2020.
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system (these are not samples from bottles!) during CalCOFI cruises while on station. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence. ALF data are merged with CTD and bottle data that were collected by the CalCOFI group.
Continuous passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE-CalCOFI Augmented cruises in the California Current System, 2012 - 2020
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system during CalCOFI cruises. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence.
Continuous passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE process cruises in the California Current System, 2012 - 2019 (ongoing).
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system during CCE process cruises. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence.
Investigation on the Use of Passive Microclimate Frames in View of the Climate Change Scenario
<p>Passive microclimate frames are exhibition enclosures able to modify their internal climate in order to comply with paintings’ conservation needs. Due to a growing concern about the effects of climate change, future policies in conservation must move towards affordable and sustainable preservation strategies. This study investigated the hygrothermal conditions monitored within a microclimate frame hosting a portrait on cardboard with the aim of discussing its use in view of the climate expected indoors in the period 2041–2070. Its effectiveness in terms of the ASHRAE classification and of the Lifetime Multiplier for chemical deterioration of paper was assessed comparing temperature and relative humidity values simultaneously measured inside the microclimate frame and in its surrounding environment, first in the Pio V Museum and later in a residential building, both located in the area of Valencia (Spain). Moreover, heat and moisture transfer functions were used to derive projections over the future indoor hygrothermal conditions in response to the ENSEMBLES-A1B outdoor scenario. The adoption of microclimate frames proved to be an effective preventive conservation action in current and future conditions but it may not be sufficient to fully avoid the chemical degradation risk without an additional control over temperature.</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
Data from: Species-specific effects of passive warming in an Antarctic moss system
Polar systems are experiencing rapid climate change and the high sensitivity of these Arctic and Antarctic ecosystems make them especially vulnerable to accelerated ecological transformation. In Antarctica, warming regions result in a mosaic of ice-free terrestrial habitats dominated by a diverse assemblage of cryptogamic plants (i.e. mosses and lichens). Although these plants provide key habitat for a wide array of microorganisms and invertebrates, we have little understanding of the interaction between trophic levels in this terrestrial ecosystem, and whether there are functional effects of plant species on higher trophic levels that may alter with warming. Here, we used Open Top Chambers (OTCs) on Fildes Peninsula, King George Island, Antarctica, to examine the effects of passive warming and moss species on the abiotic environment and ultimately on higher trophic levels. For the dominate mosses, Polytrichastrum alpinum and Sanionia georgicouncinata, we found species-specific effects on the abiotic environment, including moss canopy temperature and soil moisture. Additionally, we found distinct reproductive shifts in P. alpinum plants under warming compared to mosses without warming, and invertebrate communities in this moss species were strongly correlated with plant reproduction. Mosses under warming had substantially larger total invertebrate communities, and some invertebrate taxa were influenced differentially by moss species. However, warmed moss plants showed lower fungal biomass than control moss plants, and fungal biomass differed between moss species. Our results indicate that continued warming will differentially impact the reproductive output of Antarctic moss species, potentially altering terrestrial ecosystems dynamics from the bottom up. Understanding these effects requires clarifying the foundational, mechanistic role that individual plant species play in mediating complex interactions in Antarctica's terrestrial food-webs.
Simulations of passive diffusion through the nuclear pore complex
<p>Repository for simulations of passive diffusion through the nuclear pore complex, associated with the manuscript:</p> <p>Simple rules for passive diffusion through the nuclear pore complex. Timney B<em>, </em>Raveh B, Mironska R, Trivedi JM, Kim SJ, Russel D, Wente SR, Sali A, and Rout MP Journal of Cell Biology (2016) DOI: 10.1083/jcb.201601004</p>
Primary Sea Ice Edge from Satellite Passive Microwave Observations
<p>These are matlab output files with smoothed and unsmoothed primary ice edges around Antarctica. The primary ice edge is defined as the northernmost contour of 15% sea ice concentration, and defines the outer boundary of sea ice extent. The brightness data come from three satellites; SSM/I, AMSR-E, and AMSR2 and are converted to sea ice concentrations with the NASA Team 2 algorithm and the ARTIST algorithm.</p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
Data for: Upcycling Chips‐Bags for Passive Daytime Cooling
<p>The data include all raw data regarding the employed characterization techniques as discussed in the affected publication. These comprise: optical spectroscopy, indoor and field test measurements for passive cooling characterization, RAMAN spectroscopy and scanning electron microscopy</p>
Dataset for the publication: Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine
<div> <p>The dataset covers the research data of the publication "Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine" in International Journal of Engine Research (DOI: 10.1177/14680874211053168).</p> </div>
Data for: Mapping the Limits of Passive Samplers in Water: Chemical Space Coverage Using Nontargeted LC-HRMS Analysis
<p>This dataset provides files for passive samplers nad blanks analyzed by LC-HRMS fullscan DIA MS2.</p> <p>Excel file provides information about passive samplers, sampling site and sample files.</p>
High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx
<p>This page contains selected data from the peer-reviewed paper "High Performance Predictable Quantum Efficient Detector Based on Induced-Junction Photodiodes Passivated with SiO2/SiNx" published in Sensors by Ozhan Koybasi.</p> <p>Description of attached files:</p> <p>Figure 5. Simulated p-polarization reflectance as a function of wavelength for PQEDs mounted in trap configuration with an angle of 15° between the diodes. In this configuration the light beam undergoes 7 reflections: one at 0° degree and two reflections at 15°, 30°, and 45°. The reflectance is reported for six different thicknesses of the SiNx.</p> <p>Figure 6. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness for PQEDs mounted in trap configuration with an angle of 15° between the diodes.</p> <p>Figure 7. Maximum and mean values evaluated in the wavelength interval 400–850 nm of the p-polarization reflectance as a function of SiNx thickness, when a buffer layer of 6 nm SiO2 is depos-ited before SiNx, for PQEDs mounted in trap configuration with an angle of 15° between the di-odes.</p> <p>Figure 8. Effective lifetime τeff vs. excess carrier density (Δn) for samples prepared with passivation processes described in Table 1.</p> <p>Figure 9. Photoluminescence (PL) lifetime images of samples prepared with the passivation processes described in Table 1.</p> <p>Figure 10. Capacitance—voltage (C—V) measurement results of MIS capacitors prepared with the passivation processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1, at a frequency of 1 kHz.</p> <p>Figure 11. Injection dependent effective minority carrier lifetime τeff (Δn) of test samples passivated with processes E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table 1 with simu-lation fits to extract SRV and τbulk.</p> <p>Figure 12. Simulated IQD as a function of reverse bias voltage for p-type inversion-layer photodi-ode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a wavelength of 488 nm.</p> <p>Figure 13. Simulated IQD as a function of wavelength for p-type inversion-layer photodiode that would be fabricated with passivation E2 (6 nm SiO2+ 65 nm SiNx) and E6 (65 nm SiNx) as described in Table I. The simulations were performed at a reverse bias voltage of 5 V.</p> <p>Figure 16. Spatial uniformity of optical power responsivity of the PQEDs with SiO2/SiNx stack photodiodes P18-55-45 (a) and P18-54-44 (b).</p> <p> </p>
Passive RFID indoor location dataset
<p>We use four monostatic UHF antennas that operate in the frequency range from 902 to 928 MHz with a 6 dBi gain (isotropic antenna gain) for the implementation environment preparation. The equipment we used to perform the readings was the ThingMagic Mercury 6, a high-performance UHF RFID reader, supporting up to four monostatic antennas, digital inputs and outputs, and a Wi-Fi connection. Both devices are commercially available.</p> <p>We affixed 400 labels to objects placed side by side on the shelves in an auto parts store. The distance between the antennas and tags was 115 cm, and the distance between the antenna group was 250 cm. The reader interrogates the tags every 5 seconds and organizes the input information in a data collection formed by TagID, RSSI, Read Count (RC), True_x, and True_y. This interrogation time was defined by the minimum limit at which all tags were identified at least once by the antenna. In order to carry out data collection, the objects were placed in known positions. We performed 100 readings on each target tag, measuring the RSSI and the RC of each of the four antennas, totaling 40,000 readings. </p>
Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Phocoena phocoena) population using passive acoustic monitoring
<p>Knowing the abundance of a population is a crucial component to assess its conservation status and develop effective conservation plans. For most cetaceans, abundance estimation is difficult given their cryptic and mobile nature, especially when the population is small and has a transnational distribution. In the Baltic Sea, the number of harbour porpoises (<i>Phocoena phocoena</i>) has collapsed since the mid-20<sup>th</sup> century and the Baltic Proper harbour porpoise is listed as Critically Endangered by the IUCN and HELCOM; however, its abundance remains unknown. Here, one of the largest ever passive acoustic monitoring studies was carried out by eight Baltic Sea nations to estimate the abundance of the Baltic Proper harbour porpoise for the first time. By logging porpoise echolocation signals at 298 stations during May 2011-April 2013, calibrating the loggers' spatial detection performance at sea, and measuring the click rate of tagged individuals, we estimated an abundance of 71-1,105 individuals (95% CI, point estimate 491) during May-October within the population's proposed management border. The small abundance estimate strongly supports that the Baltic Proper harbour porpoise is facing an extremely high risk of extinction, and highlights the need for immediate and efficient conservation actions through international cooperation. It also provides a starting point in monitoring the trend of the population abundance to evaluate the effectiveness of management measures and determine its interactions with the larger neighbouring Belt Sea population. Further, we offer evidence that design-based passive acoustic monitoring can generate reliable estimates of the abundance of rare and cryptic animal populations across large spatial scales.</p>
The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability
<p>Data set of the article: The Influence of Active and Passive Motion Experience on Infants’ Visual Prediction Ability</p>
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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.