Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

[Bmim]Cl limiting current density - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.

Dataset contains results obtained during the NCN project SONATA-17, grant no. 2021/43/D/ST8/02776. The file presents results of the influence of [Bmim]+ ion concentration and linear flow velocity on the limiting current density during electrodialysis process.

opencc-by-4.0Dec 2023View details →

[Omim]Cl recovery by ED - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.

Dataset contains results obtained during the NCN project SONATA-17, grant no. 2021/43/D/ST8/02776. The file presents results of the effect of ED parameters on IL recovery.

opencc-by-4.0Jun 2023View details →

[Bmim]HSO4 recovery by ED - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.

Additional files for 'spatial scales of tropical isotope change' project

Additional files for tropical-d18O-scales repository on github. Associated code and other files are associated with DOI: 10.5281/zenodo.11053701.

Datos de la investigación: La voz del rehén: adapting creative sustainable development goal-based physical-artístico project during COVID pandemic situation

This article assesses the effects on creativity of an interdisciplinary physicall-artistic programme and their effects during pandemic context. The sample comprised 97 individuals –54 men and 43 women– with an average age of 21.37±1.56, all of whom were studying a Primary Education Teacher Training Degree at Zaragoza University –Spain–. It was a pre-experimental study with measures pre-post programme –adapted to pandemic situation, replacing the acrosport content to skipping rope, juggling and aerial dance–. The creative skills assessment instrument was the PIC-A. A Student’s T-test and ANOVA test were performed to discover significant differences in pre-post in both academic years. Results showed that: i) participants obtained significant improvements in total score of creativity in both academic years; ii) the adaptations made to the intervention programme during the pandemic year were effective in developing creativity. These studies confirm the importance of incorporating interdisciplinary creativity programmes into the university system.

BATTERY2LIFE Project Logo

BATTERY2LIFE Project Logo

Jupyter Notebook and comprising data for GRL2023GL106264R: Understanding the Cascade: Removing GCM biases improves dynamically downscaled climate projections

This notebook and attendant files allows users to interface with a small subset of the data used to create the data in GRL2023GL106264R. Also feel free to check out the overall description of the non-bias corrected dynamically downscaled GCMs in WUS-D3 here: https://zenodo.org/records/10635867. This DOI also contains version of WRF 4.1.3 allowing for yearly CH4, CO2, and N2O updates, as well as a 360-day calendar version.

Dataset for "Application of Generalized - Aurora Computed Tomography to the EISCAT_3D project"

Dataset for "Application of Generalized - Aurora Computed Tomography to the EISCAT_3D project"

Webinar: Towards bio-based paints and coatings – Fundamentals and an introduction to the PERFECOAT project

The European paints and coatings market is projected to grow from USD 37.2 billion in 2023 to USD 41.6 billion by 2028 (Markets and Markets, 2023). While a majority of currently used ingredients are fossil-based, prospects show an increasing demand for sustainable paints and coatings, especially in the professional and DIY (Do it yourself) industries. To enable a smooth transition from fossil to sustainable products, the industry is in desperate need for high performance bio-based solutions especially for bio-polymers and lipids, binders, fillers, pigments and additives like functional nanomaterials. These major component groups combined account for nearly 70% of the ingredients (American Coatings Association, 2023). In the first two years of examination, the EU funded research project PERFECOAT successfully developed innovative bio-based solutions, from bio-waste and agricultural residues. These will be tested in the formulation of three use-cases: UV-curable clear coatings for wood, waterborne trim paints for DIY, and waterborne wall paints. At this project stage, the partners successfully developed bio-based polymers and lipids, binders, fillers, fungal pigments and dyes, as well as different additives like functional nanomaterials. These cover a variety of performance requirements like paint texture and grip, clarity, flexibility, UV resistance (no yellowing) as well as drying and curing time.  In the first webinar “Towards bio-based paints and coatings – Fundamentals and an introduction to the PERFECOAT project” international experts will introduce the basics of paint and coatings formulation and address the potential of bio-based solutions in this field. A historic overview will address paint and coating applications and discuss the performance requirements for newly developed solutions and challenges in novel applications. Curious? Register for the first webinar with speakers: <ul> <li>Alexander Wetzel (SINTEF), PERFECOAT project-coordinator</li> <li>Christine Louis (Evonik Coating Additives)</li> <li>Ilker Kocabiyik (Organik Kimya)</li> </ul>

Data from the micrometeorological tower of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)

Micrometeorological tower data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate CO2 fluxes (FCO2). A 9 m high metal micrometeorological tower was installed on the bow of the Polar Ship Almirante Maximiano, 8.75 m above sea level, for sampling atmospheric variables. In the tower, the Motion Pack II was installed, on the main shaft of the tower, to determine the ship's movement in the orthogonal directions xp, yp and zp of the platform's coordinate system, at a frequency of 20 Hz. As well as, a GPS (Global Positioning System) and an electronic compass, to determine the ship's geographic position and speed. The movement of the ship influences wind speed measurements, and as a solution, a wind speed correction was carried out. An IRGASON sensor (Campell Scientific®) was placed on the secondary shaft of the tower, configured to perform measurements at 20 Hz. The IRGASON has an open path infrared gas analyzer that measures the concentrations of CO2 and water vapor (H2O), a three-dimensional sonic anemometer, which measures the 3 vector components of the wind, and a thermohygrometer that measures air temperature and humidity. From the IRGASON collections, it is possible to calculate the CO2 flux using the Vortex Covariance method.

opencc-by-4.0Dec 2023View details →

Project Section: Searching for Mutations Leading to Isoniazid Resistance in TB (HackBio)

Introduction Tuberculosis (TB) remains a global health concern, and the emergence of drug-resistant strains poses a significant challenge to effective treatment. Isoniazid is a key first-line drug used in the treatment of TB, and resistance to this drug can compromise the success of therapy. This project aims to identify and characterise genetic mutations associated with isoniazid resistance in Mycobacterium tuberculosis, the causative agent of TB. Datasets: Here Task: <ul> <li>Given the datasets above, perform the complete variant calling and annotation pipeline to specifically identify the following known mutations in Mycobacterium tuberculosis (against Isoniazid). </li> <li>Provide pretty IGV visualisation of mutations in katG sequences (Position: 2156111 - 2153889) across all the samples. N/B: katG mutations are prevalent in isoniazid resistance.</li> <li>Also, explore associated biological pathways to understand the molecular mechanisms of isoniazid resistance.</li> </ul> Expected Output: Kindly submit a comprehensive project report, including methods, results, and interpretations.

LI-COR (LI-850) sensor data obtained by the Antarctic Modeling Observation System (ATMOS) project during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of CO2 (pCO2)

LI-COR (LI 850) sensor data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of seawater CO2 (pCO2sea) The LI-850 carbon dioxide analyzer was installed in the laboratory aft of H41 together with a balancer to measure the CO2 concentration of the water. The collection system occurs as follows: the ship's saltwater piping system collects seawater, when this water enters the balancer it generates turbulence. The turbulence generated causes the CO2 present in the water to come into balance with the air. The air that comes out of the balancer is pumped into the LI-850, by its internal pump, and thus, the equipment measures the concentration of CO2 present in the water. To ensure that the air inside the balancer is actually balanced with the seawater, the air leaving the LI-850 is pumped back into the balancer, closing the circuit. From these data it is possible to calculate pCO2sea.

Dataset for: Advancing projections of Crown-of-Thorns Starfish to support management interventions

<h3>Abstract</h3> <div> Regular outbreaks of corallivorous Crown of Thorns Starfish (Acanthaster spp; CoTS) occur on the Great Barrier Reef (GBR) and are one of the leading drivers of coral mortality. Understanding the disparities between real-world observations and model predictions of CoTS densities is crucial for refining population modelling and developing effective control strategies. Using a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR), we compare model predictions of CoTS densities to manta tow survey observations. We also incorporate a new zone-specific CoTS mortality rate to account for differences in predation of CoTS between fished and protected reefs. We found high congruence between predicted CoTS densities and observations: ~81% of categorical reef level CoTS densities were either the same density level or only differed by one level, however underpredictions increased as observed densities increased. The zone-specific CoTS mortality rate reduced severe underpredictions from 7.1% to 5.6%. Underpredictions are a key concern for reef managers as they indicate potential missing outbreaks where targeted culling efforts are necessary and may lead to an underestimation of the coral loss attributed to CoTS outbreaks. Reef protection status was an important driver of prediction accuracy, suggesting it plays a role in determining CoTS densities, emphasising the importance of further research on in situ CoTS mortality rates. The location of a reef inside or outside the “initiation box”, a speculative area of primary outbreaks on the GBR, was also important, with exact predictions more likely to occur outside the box. Accurately modelling initiation box dynamics is challenging owing to limitations of empirical data on CoTS outbreaks, but this highlights the need for focussed research on these dynamics to enhance overall predictive accuracy. Other spatial factors, such as region and shelf position, also contributed to the variance between observations and predictions, underscoring the importance of the spatial-temporal context of each observation. In conclusion, this study validates our CoTS population modelling efforts, showcasing a high congruence between CoTS density predictions and real-world observations. CoTS observations can help refine predictions and guide targeted control against CoTS populations and outbreaks, contributing to effective ecosystem management for long-term resilience of the GBR. </div> <h3>Methods</h3> <div> Data are mean CoTS density (per manta tow) observations and predictions for individual reefs and years on the Great Barrer Reef. Observations come from several sources (CCP, LTMP, FMP) and predictions come from a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR).  </div> <h3>Subject keywords</h3> Earth and related environmental sciences, adaptive management, coral reef, Great Barrier Reef, individual-based model, Marine Invertebrate, pest management, spatial simulations <h3>Funding</h3> <div> CoTS Control Innovation Program </div> <div> <h2>README: Dataset for: Advancing projections of Crown-of-Thorns Starfish to support management interventions</h2> <a href="https://doi.org/10.5061/dryad.31zcrjdtq">https://doi.org/10.5061/dryad.31zcrjdtq</a> This dataset is for a paper that compares CoTS density observations to predictions for individual reefs on the Great Barrier Reef in individual years. CoTS manta tow observations derive from the CoTS Control Program (CP), Field Management Program (FMP), and the AIMS Long Term Monitoring Program (LTMP). Where multiple observations exist from the same observation source, they are averaged at the reef-level in that year. If multiple observations exist from different sources, they are kept separate. Predictions are generated by a spatially explicit ecosystem model of the Great Barrier Reef (ReefMod-GBR). <h3>Description of the data and file structure</h3> Observed and predicted CoTS densities are compared to determine prediction accuracy of the ecosystem model. Both observed and predicted CoTS per tow values were categorised as follows: 0 = None (Level 1); ≤ 0.1 = No Outbreak (Level 2); 0.11 - 0.22 = Potential Outbreak (Level 3); 0.22 - 1.0 = Established Outbreak (Level 4); 1.0 - 3.0 = Severe Outbreak (Level 5); > 3.0 = Extreme Outbreak (Level 6). The level of each observation was then compared to the level of each prediction and the difference calculated to determine prediction accuracy. Different variables were extracted as potential predictors of CoTS prediction accuracy. As such, the dataset includes the following for each observation/prediction comparison: 1) RM_ID = an individual ID for each of the 3806 reefs that we model in our ecosystem model. 2) YEAR = year as an integer. 3) LAT and LON = the latitude and longitude of the reef. 4) GBRMPAID and GBR_NAME = identifiers for each reef from the Great Barrier Reef Marine Park Authority. 5) REGION: 1 = North, 2 = Central, 3 = South 6) SHELF: 1 = Inner, 2 = Middle, 3 = Outer 7) GZ = Whether a reef is protected (i.e., in a green zone = 1) or not (i.e., in a blue zone = 0). 8) IB = Whether a reef is inside the CoTS initiation box (1) or not (0). 9) GEOM_CH_KM2 = the area (km2) of coral habitat for that reef 10) OBS_SOURCE = the source of the CoTS observation (1 = LTMP, 2 = FMP, 3 = CP) 11) OBS_n = the number of observations that went into calculating the reef-level mean CoTS per tow for that year 12) OBS_COTS_CAT = observed CoTS density as a categorical level from GBRMPA 13) OBS_COTS = mean CoTS per tow, and OBS_COTS_1YB = mean COTS per tow at that reef in the preceding year 14) OBS_CC, OBS_CC_1YB = mean coral cover at that reef from that OBS_SOURCE, and OBS_CC_1YB = in the year preceding it. 15) PRED_COTS_CAT = predicted CoTS density as a categorical level from GBRMPA 16) PRED_COTS, PRED_COTS_1YB, PRED_COTS_2YB, PRED_COTS_3YB = predicted CoTS per tow densities at the current year, and in the one, two, and three years preceding it. 17) PRED_CC, PRED_CC_1YB, PRED_CC_2YB, PRED_CC_3YB = predicted coral cover (%) at the current year, and in the one, two, and three years preceding it. 18) PRED_ACRO, PRED_ACRO_1YB, PRED_ACRO_2YB, PRED_ACRO_3YB = predicted Acropora cover (%) at the current year, and in the one, two, and three years preceding it. Acropora is the preferred food of CoTS. 19) PRED_INSTR, PRED_INSTR_CUMU = incoming strength of CoTS larvae calculated as the CoTS larval input multipled by the size of the reef area. PRED_INSTR_CUMU is the sum of this value over the three years previous to the current year. 20) TOTAL_OBS = where data exist, the CoTS model ReefMod is forced with manta tow survey observations from the CP, FMP, and LTMP which override model predictions at individual reefs/years. This shows the total number of observations that have been used to force the CoTS predictions for this reef in all preceding years. 21) TIME_SINCE_OBS = similar to TOTAL_OBS, but gives the number of years since an observation last forced the CoTS prediction for this reef. 22) PRED_ERR_COTS = the difference in categorical CoTS density levels between observations and predictions. 23) PRED_ERR_CC = observed and predicted coral cover was categorised into AIMS coral cover categories: 0 = 0%, 1 = 0 - 10%, 2 = 10 - 30%, 3 = 30 - 50%, 4 = 50 - 75%, 5 = 75 - 100%. The difference in coral cover category between observations and predictions was then compared. 24) DIFF_CC = the difference in the % coral cover between the observations and predictions. 25) DIFF_CC_CUMU, DIFF_CC_CUMU_N = same as DIFF_CC except the cumulative % difference over the three preceding years, and the number of observations that went into calculating the %. </div>

Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"

Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders: MethodA_proj/ MethodB_proj/ MethodC_proj/ MethodD_proj/ MethodE_proj/   Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details): FUTUREverif/

FAIR-IMPACT Synchronisation Force 2023 edition: preliminary data gathered from projects and intiatives

Building on the successful Synchronisation Force <a href="https://fair-impact.eu/events/synchronisation-force-events/synchronisation-force-1st-workshop-november-2022">workshop 2022</a>, the 2023 edition of the Synchronisation Force workshop aimed to discuss common challenges and priorities related to turning the FAIR principles into practice. The workshop took place as a series of seven virtual sessions between 2 November 2023 and 8 February 2024, with the five core sessions between 27 November and 7 December 2023. Invited to this workshop were selected, key FAIR representatives of projects and initiatives in the EOSC framework. In this record we saved the preliminary information gathered from the workshop participants collected via a survey. The information were used as preliminary discussion points for each of the five sessions organised, and were considered useful top ensure an interactive discussion.

Hellenistic-Roman bioarchaeological dental disease data - MetaMobility project

This database covers the bioarchaeological data we could extract on Hellenistic-Roman period dental diseases in the Central and Eastern Mediterranean and Middle East. The database was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.

Hellenistic-Roman bioarchaeological mechanical stress data - MetaMobility project

This database covers the bioarchaeological data we could extract on Hellenistic-Roman period skeletal markers of mechanical stress in the Central and Eastern Mediterranean and Middle East. Specifically, it covers degenerative joint disease (osteoarthritis) in the major appendicular joints of the skeleton, intervertebral disc disease (spinal arthritis on the vertebral bodies and apophyseal joints), Schmorl's nodes and entheseal changes. The database was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.

Hellenistic-Roman bioarchaeological mobility data - MetaMobility project

This database covers the bioarchaeological data we could extract on Hellenistic-Roman period human mobility in the Central and Eastern Mediterranean and Middle East. In addition, it includes studies that are not about mobility per se but they provide metric or nonmetric data that could be used in biodistance studies. The database was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.

Hellenistic-Roman bioarchaeological physiological stress data - MetaMobility project

This database covers the bioarchaeological data we could extract on Hellenistic-Roman period physiological stress markers in the Central and Eastern Mediterranean and Middle East. Specifically, it covers cases of cribra orbitalia, porotic hyperostosis, enamel hypoplasia and periostitis. The database was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.

Hellenistic-Roman zooarchaeology data - MetaMobility project

This database includes zooarchaeological data from the Eastern Mediterranean and Middle East dating to the Hellenistic and Roman periods. It was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.