Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.

Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from  GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090.  At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were  based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.

opencc-by-4.0Jan 2024View details →

CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.

Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.

opencc-by-4.0Sep 2021View details →

Crowd4SDG - Crowdsourced image classification and damage assessment

This data set contains crowdsourced classification and damage assessment of images of an earthquake extracted from social media.   A data set of 907 images posted on Twitter related to the 2019 Albanian Earthquake, that are filtered and pre-classified using an automated technique is cross-validated for accuracy by two different crowds. One, digital humanitarian volunteers using the crowdsourcing platform <a href="http://www.crowd4ems.org">CROWD4EMS</a> and another, paid micro-taskers of the Amazon Mechanical Turk. In order to compare and evaluate the efficiency and accuracy of the volunteers and the paid micro taskers, ground truth is established with the help of a team of experts, who validated the same set of data.  Parameters considered for volunteer contributions: The dataset was imported to the Crowd4EMS platform for Crowd contribution. In the forum, each volunteer will see the image to be validated along with the tweet text and the link to the original tweet. The user has to validate whether the given image is relevant or irrelevant to the disaster. In case of doubt, the user can refer to the tutorial explaining the relevance or skip the task. Once the image's relevance is validated, the user will be asked to label the severity of the impact, as seen in the image. The Automated algorithm has pre-classified the images as severe and minimal damage. The Crowd4EMS platform lets the volunteer label them as 'severe damage,' moderate damage',' minimal damage', and' no damage'. Each task has to be answered at least three times, and the final consensus is taken as per the inter-rater agreement.  Parameters considered for micro-taskers contribution: The dataset was imported to the Amazon Mechanical Turk platform for Crowd contribution. In the platform, each worker will see only the image that is to be categorised as follows: The user has to validate whether the given image depicts severe damage, moderate damage, minimal damage, no damage or irrelevant to the disaster. Each task has to be answered at least ten times, and the final consensus is taken as per the inter-rater agreement.  Acknowledgements: We want to thank Muhammad Imran of Qatar Computing Research Institute for sharing their pre-filtered social media imagery dataset on the Albanian earthquake from the Artificial Intelligence for Disaster Response (AIDR) Platform. We would also like to extend our gratitude to the volunteers for their contribution on the Crowd4EMS Platform.

opencc-by-4.0Oct 2021View details →

Macroeconomic assessment of Climate Change Impacts

Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts

opencc-by-4.0Jul 2023View details →

Dataset of the manuscript "Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost."

Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: "readme.csv" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. "earthworm_N_biomass.csv" is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled "FTIR_spectra.csv" is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. "litter_chemical_composition.csv" is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. "litter_mass_loss.csv" contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . "mesofaunal_com.csv" are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.

opencc-by-4.0Oct 2023View details →

Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education

Overview This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.Study Context, Location and Participants To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023. The study was conducted in Switzerland, specifically within the Ticino canton. The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). Data Collection Data collection was integrated into a validation module of the app.  Sessions required manual input for details like date, canton, and school information.  Students' details, anonymised for privacy, encompassed their gender and date of birth.  Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.Data Features The dataset comprises the following files:<ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.Usage & Ethics In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment.  Initial authorisations were secured from school administrators, teachers, and parents.  Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders. REFERENCES[1] A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a>[2] Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a>[3] Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a>[4] Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a>

openCC (other)Jun 2025View details →

Data associated with the 2019 Freshwater Oil Spill Remediation Study (FOReSt) assessing the use of enhanced Monitored Natural Recovery (eMNR) and shoreline washing agent (SWA) of diluted bitumen spills conducted in shoreline enclosures at the IISD Experimental Lakes Area, ON, Canada from 2019 to 2020

The following package includes data from the 2019 Freshwater Oil spill Remediation Study (FOReSt) at the IISD Experimental Lakes Area studying the use of enhanced monitored natural recovery (eMNR) and shoreline washing agent (SWA) as a secondary remediation method for diluted bitumen spills in freshwater shoreline enclosures. This package includes data tables on polycyclic aromatic compound chemistry in water and sediments, basic water quality, nutrient chemistry, and tritium chemistry monitored in the experimental and reference enclosures, and lake reference sites over the duration of the study. Data included in this package was first collected and used in the paper by Palace et al., titled Polycyclic aromatic compounds in freshwater ecosystems following non-invasive remediation of controlled diluted bitumen spills: The Freshwater Oil Spill Remediation Study (FOReSt) at the Experimental Lakes Area, Canada.

openCC (other)Sep 2025View details →

Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.

This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.

openCC (other)Jan 2026View details →

The 2021 Freshwater Oil Spill Remediation Study (FOReSt), assessing the use of enhanced Monitored Natural Recovery (eMNR) of conventional heavy crude oil spills conducted in freshwater shoreline enclosures at the IISD Experimental Lakes Area, ON, Canada from 2021 to 2022.

The following package includes data from the 2021 Freshwater Oil spill Remediation Study (FOReSt) at the IISD Experimental Lakes Area studying the use of enhanced monitored natural recovery (eMNR) as a secondary remediation method for conventional heavy crude oil spills in freshwater shoreline enclosures. This package includes data tables on polycyclic aromatic compound chemistry in water and sediments, basic water quality, nutrient chemistry monitored in the experimental and reference enclosures, and lake reference sites over the duration of the study. As well as tables detailing enclosure metrics (depth), tritium chemistry, and a treatment key. Data included in this package was first collected and used in the paper by Stanley et al., titled Rapid Chemical Remediation of Freshwater Enclosures Treated with Conventional Heavy Crude Oil Spills Followed by Enhanced Monitored Natural Recovery

openCC (other)Feb 2024View details →

Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009

Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.

openCC0May 2023View details →

NGE01 Chronic Addition of Nitrogen Gradient Experiment (ChANGE): Assessing threshold responses of plant community composition and ecosystem processes at Konza Prairie

Chronic nutrient additions can lead to drastic shifts in the plant community through time, both within tallgrass prairie in other grassland ecosystems worldwide. Nutrient addition experiments have answered many questions about patterns of diversity loss and community shifts; however, the level of nutrients which must be added to cause community shifts is unknown. To date, all nitrogen (N) addition experiments at Konza have added 10 g m-2 (e.g., NutNet Plots; Phosphorus (P) Plots; Belowground Plots), yet current rates of N deposition are one-tenth of that level. Even predicted rates of future N deposition in grasslands are not expected to exceed 5 g m-2 by the year 2050 and will likely be around 2 g m-2 for most of the US. This mismatch begs the question will 10 g/m2 affect grasslands the same way 2 or 5 g m-2 will? There are two main goals for this long-term experiment (1) to identify the nutrient threshold needed to drive plant community change with nutrient additions, and (2) to determine what factors underlie those threshold responses (build up of nutrients, mycorrhizal loss, invertebrate herbivory). Konza ChANGE is part of a multi-site experiment spanning grasslands on two different continents: North America – tallgrass prairie (KNZ) and shortgrass steppe (SGS), and China – three sites in Inner Mongolia. By including multiple grasslands, we expand our ability to make generalizations about how grasslands are affected by N additions, and whether thresholds, if they exist, vary with precipitation, natural nutrient availability, and species identity/composition. Research Questions: (1) Do ecosystems have N tolerance thresholds above which community composition will change, and does that differ between grassland types (i.e. mesic and xeric grasslands)? (2) Does adding a large amount of nutrients in one season result in an equivalent community change as adding a small amount over multiple years? (For example does 5 g m-2 for 6 years create the same community change as

openCC (other)Jul 2024View details →

Minneapolis-St. Paul (MSP) Metro Area bee lawn assessments and bumble bee survey, 2023

Eighty residential lawns were recruited across the seven-county Minneapolis-St. Paul Metropolitan Area to take part in a survey of bumble bee species, vegetation and soil characteristics across lawns with differing management regimes. Lawns were selected to capture a range across the urban to rural gradient (based on percent of impervious surface) and household incomes and ethnicity at the Census block group level. Approximately half the lawns were characterized as "traditional" lawns, while half were considered "bee lawns" based on initial plant and bee community data. Three non-residential sites were included in this study: Katharine Ordway Natural History Study Area, The Minnesota Bell Museum of Natural History, the University of St. Thomas Stewardship Garden. The Bombus spp. composition was surveyed NON-LETHALLY at each site twice in the summer, with six endangered rusty-patched bumble bees (Bombus affinis) observed in 2023. Each property was also surveyed for plant species composition and soil moisture. Additional soil and site characteristics will accompany this dataset at a later date. Locations of the lawns are jittered randomly to protect the privacy of participating residents in the study.

openCC (other)Nov 2022View details →

2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.

Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).

openCC (other)Dec 2022View details →

Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment

Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.

opencc-by-4.0Aug 2020View details →

MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research

Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604

opencc-by-4.0Dec 2020View details →

IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4

The IPBES data management tutorials are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples. The chapter on Examples of implementing the IPBES data management Policy contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here: <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a> This session Literature review from the Global Assessment chapter 4 walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment.

opencc-by-4.0Dec 2020View details →

Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments"

This dataset from the publication entitled "Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments" contains simulated data of magnetic hyperthermia treatments for three different indications: colorectal cancer, prostate cancer and head & neck cancer. Since the aim of the study is to evaluate the risk of thermal damage caused by the collateral heating of two common types of passive prostheses (hip and dental implants), eddy currents induced in these implants upon interacting with the externally applied ac field during treatment have been computed for all the evaluated regions. Two different alloys for the implants have been considered for each case as well: Ti6Al4V and CoCrMo. At the same time, besides temperature, the specific abosorption rate (SAR) have been also computed to work out the energy deposition in tissues. Calculations have been carried out using a het exchange model with and without thermoregulation. log-log SAR vs T plots have been obtained and proposed as a quick means to pre-check treatment feasibility in each patient. These graphs are thought to be included in treatment planning prior to the clinical procedure. Other parameters taken into account have been the treatment time (5 and 30 minutes), and the maximum tolerable temperature threshold (1 or 5 ºC, as indicated by the ICNIRP commission), all for three main types of tissues, namely fat, bone and muscle. Each tissue have been simulated using three different field intensities (5, 10 and 15 mT). The field frequency has been 300 kHz in all cases. The files "Dataset_description.doc" and "file_scheme.txt" contain the structure and description of the files that make up the dataset. UPDATES FROM PREVIOUS VERSIONS: simulations of the dental implant without thermoregulation have been added.

opencc-by-4.0Oct 2023View details →

Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion

Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges

opencc-by-4.0Jul 2023View details →

[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing

This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).The supplementary information consists of three files:SI 1 - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.SI 2 - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.