Find research datasets worth reusing

Data food baskets Eusocialcit

The dataset is linked to the EuSocialCit working papers on the construction of cross-national comparable food budgets. Nutritionists and researchers in four European countries have calculated comparable food budgets for households living in an urban context in Belgium, Finland, Hungary and Spain. The budgets are used as a social indicator in a tentative exercise to assess the affordability of a healthy and sustainable diet. The datafile contains: <ul> <li>comparable priced food baskets (divided according to food categories and including kitchen equipment) for 16 person types and based of EFSA DRV’s an national Food based dietary guidelines</li> <li>comparable sustainable food baskets (divided according to food categories and including kitchen equipment) for 2 person types (adult man and adult woman on active age) based on EAT lancet recommendations</li> <li>Harmonised sustainable food baskets (divided according to food categories and including kitchen equipment) for  person type (adult man on active age)</li> <li>Priced basket of kitchen equipment for a base household + additional prices for extra household types</li> </ul>

Dataset of "Perovskite QDs embedded in polymer as a wavelength-shifting layer for UV-sensitized silicon sensors"

Detection of UV radiation is becoming increasingly important for many applications. Here we present novel UV sensor construction on the basis of standard Si detector modification. Wavelength shifting mechanism is achieved by the luminescence effect of perovskite quantum dots embedded in polymer layers. We comprehensively characterize these composite materials, various sensor modification routes and the  optical properties of UV-enhanced visible sensors. Modified S1227-16 BG silicon photodiodes and S13360-1375 CS MPPC photodetectors with the enhanced UV response are successively manufactured.micrographs; cross sections of model simulation or prediction (MSP).

List of TEI rolename annotations in the ISicily EpiDoc corpus

This CSV file details every instance of a 'roleName' tag in the I.Sicily (sicily.classics.ox.ac.uk) EpiDoc TEI files, reporting the ID number of the file in which it appears, and the value of the @type and @subtype attributes in each case - as such it serves as an index of roleName attestations in the I.Sicily dataset (also recoverable directly from the EpiDoc files). The file will be updated in future.

opencc-by-4.0Jan 2024View details →

Multilingual news article similarity dataset

This dataset contains the extended version of the authors' earlier work: <a href="../records/6507872">https://zenodo.org/records/6507872,</a> where pairs of news articles drawn from the first half of 2020 are annotated for seven aspects of similarity in the original version as well as an additional FRAME aspect: <ul> <li>GEO: How similar is the geographic focus (places, cities, countries, etc.) of the two articles?</li> <li>ENT: How similar are the named entities (e.g., people, companies, organizations, products, named living beings), excluding previously considered locations appearing in the two articles?</li> <li>TIME Are the two articles relevant to similar time periods or describing similar time periods?</li> <li>NAR How similar are the narrative schemas presented in the two articles?</li> <li>OVERALL Overall, are the two articles covering the same substantive news story? (excluding style, framing, and tone)</li> <li>STYLE Do the articles have similar writing styles?</li> <li>TONE Do the articles have similar tones?</li> <li>FRAME Do the articles have similar framing and express similar opinions?</li> </ul>

openmit-licenseApr 2024View details →

A collection of datasets for software vulnerability detection

This is a collection of datasets that are used for AI-based software vulnerability detection. All the datasets are in the .csv format and each row represents a sample. Each dataset includes a set of functions written in C and the target of each function is either 0 (non-vulnerable) or 1 (vulnerable). <ol> <li>data_C_Lin2017_test.csv: <ul> <li>Reference paper: <a href="https://dl.acm.org/doi/10.1145/3133956.3138840">Vulnerability Discovery with Function Representation Learning from Unlabeled Projects</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/DanielLin1986/function_representation_learning">https://github.com/DanielLin1986/function_representation_learning</a></li> <li>This dataset includes 44 vulnerable and 577 non-vulnerable functions from the LibPNG project.</li> </ul> </li> <li>data_C_LineVul_test.csv: <ul> <li>Reference paper: <a href="https://ieeexplore.ieee.org/document/9796256">LineVul: A Transformer-based Line-Level Vulnerability Prediction</a>, 2022.</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset">https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset</a></li> <li>This dataset includes 1055 vulnerable and 17809 non-vulnerable functions.</li> </ul> </li> <li>data_C_PrimeVul_test.csv: <ul> <li>Reference paper: <a href="https://arxiv.org/abs/2403.18624">Vulnerability Detection with Code Language</a> <a href="https://arxiv.org/abs/2403.18624">Models: How Far Are We?</a> 2024.</li> <li>Data source on GitHub: <a href="https://github.com/DLVulDet/PrimeVul">https://github.com/DLVulDet/PrimeVul</a></li> <li>From the data source, the primevul_test.jsonl was used to created this dataset.</li> <li>This dataset includes 695 vulnerable and 25213 non-vulnerable functions.</li> </ul> </li> <li>data_C_Choi2017_test.csv: <ul> <li>Reference paper: <a href="https://www.ijcai.org/proceedings/2017/0214.pdf">End-to-End Prediction of Buffer Overruns from Raw Source Code</a> <a href="https://www.ijcai.org/proceedings/2017/0214.pdf">via Neural Memory Networks</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/mjc92/buffer_overrun_memory_networks">https://github.com/mjc92/buffer_overrun_memory_networks</a></li> <li>From GitHub, all the data in trainnig_100.txt, test_1_100.txt, test_2_100.txt,test_3_100.txt,test_4_100.txt, and corresponding _labels.txt files are combined to create this dataset.</li> <li>This dataset includes 7054 vulnerable and 6946 non-vulnerable functions.</li> </ul> </li> <li>data_C_Devign_test.csv: <ul> <li>Reference paper: <a href="https://proceedings.neurips.cc/paper_files/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf">Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks</a>, 2019</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/claudios/code_x_glue_devign">https://huggingface.co/datasets/claudios/code_x_glue_devign</a></li> <li>From Hugging Face, all the data in train, validation, and test are combined to create this dataset.</li> <li>This dataset includes 12460 vulnerable and 14858 non-vulnerable functions.</li> </ul> </li> <li>data_C_Ours_{train,test}.csv: <ul> <li>This dataset is manually collected from projects on GitHub that have registered CVEs into NVD from 2002 to 2023. The 6,766 non-vulnerable code functions are extracted from the <a href="https://dl.acm.org/doi/10.1145/3607199.3607242">DiverseVul dataset</a> to increase the code diversity. </li> <li>This training set includes 5413 vulnerable and 5413 non-vulnerable functions.</li> <li>The test set includes 1353 vulnerable and 1353 non-vulnerable functions.</li> </ul> </li> </ol>

opencc-by-4.0Sep 2022View details →

Soundscape Attributes Translation Project (SATP) Dataset

The data and audio included here were collected for the Soundscape Attributes Translation Project (SATP). First introduced in Aletta et. al. (<a href="https://biblio.ugent.be/publication/8695720/file/8695735.pdf">2020</a>), the SATP is an attempt to provide validated translations of soundscape attributes in languages other than English. The recordings were used for headphones - based listening experiments. The data are provided to accompany publications resulting from this project and to provide a unique dataset of 1000s of perceptual responses to a standardised set of urban soundscape recordings. This dataset is the result of efforts from hundreds of researchers, students, assistants, PIs, and participants from institutions around the world. We have made an attempt to list every contributor to this Zenodo repo; if you feel you should be included, please get in touch. Citation: If you use the SATP dataset or part of it, please cite our paper describing the data collection and this dataset itself. Overview: The SATP dataset consists of 27 30-sec binaural audio recordings made in urban public spaces in London and one 60 sec stereo calibration signal. The recordings were made at locations as reported in Table 1 of the README.md (Recording locations), at various times of day by an operator wearing a binaural kit consisting of BHS II microphones and a SQobold (HEAD acoustics) device. Recordings were then exported to WAV via the ArtemiS SUITE software, using the original dynamic range from HDF. The listening experiment and the calibration procedure were intended for a headphone playback system (Sennheiser HD650 or similar open-back headphones recommended).  The recordings were selected from an initial set of 80 recordings through a pilot study to ensure the test set had an even coverage of the soundscape circumplex space. These recordings were sent to the partner institutions (see Table 2 of the README.md) and assessed by approximately 30 participants in the institution's target language. The questionnaire used in each assessment is a translation of Method A Questionnaire, ISO 12913-2:2018. Each institution carried out their own lab experiment to collect data, then submitted their data to the team at UCL to compile into a single dataset. Some institutions included additional questions or translation options; the combined dataset (`SATP Dataset v1.x.xlsx`) includes only the base set of questions, the extended set of questions from each institution is included in the `Institution Datasets` folder. In all, SATP Dataset v1.4 contains 19,089 samples, including 707 participants, for 27 recordings, in 18 languages with contributions from 29 institutions. Descriptions of the recordings, including GPS coordinates and sound sources, can be found in the README.md file. Format: The audio recordings are provided as 24 bit, 48 kHz, stereo WAV files. The combined dataset and Institutional datasets are provided as long tidy data tables in .xlsx files. Calibration: The recommended calibration approach was based on the open-circuit voltage (OCV) procedure which was considered most accessible but other calibration procedures are also possible (Lam et. al. (<a href="https://arxiv.org/abs/2207.12899">2022</a>)). The provided calibration file is a computer generated sine wave at 1kHz, matching a sine wave recorded using the exact same setup at SPL of 94 dB. In case of the calibration signal playback level set to match SPL of 94 dB at the eardrum, all the 27 samples should be reproduced at realistic loudness. More details on OCV calibration procedure and other options you can find in Lam et. al. (<a href="https://arxiv.org/abs/2207.12899">2022</a>) and the attached documentation. PLEASE DO NOT EXPOSE YOURSELF NOR THE PARTICIPANTS TO THE CALIBRATION SIGNAL SET AT THE REALISTIC LEVEL AS IT CAN CAUSE HARM. License and reuse: All SATP recordings are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) License and are free to use. We encourage other researchers to replicate the SATP protocol and contribute new languages to the dataset. We also encourage the use of these recordings and the perceptual data for further soundscape research purposes. Please provide the proper attribution and get in touch with the authors if you would like to contribute a new translation or for any other collaborations.

opencc-by-4.0Aug 2023View details →

Integrated database on adaptation and mitigation measures in Europe

Climate action is far from meeting the internationally agreed adaptation and mitigation goals. Even though climate action planning has increased since the Paris Agreement in 2015, the implementation rate of those plans remains low. Climate planning literature claims that accounting for long-term planning and implementation times, accurately estimating costs, identifying synergies and trade-offs between measures, or considering justice and equity issues might increase the quality of climate plans and facilitate the further implementation of climate actions. Also, there is no uniform way of responding to the climate crisis. Existing climate action databases typically focus on a particular type of response, sector, hazard, or type. In parallel, national governments and international initiatives provide tools and guidelines to facilitate the development of climate action plans. However, the primary climate action recording and monitoring initiatives and projects do not share the same framework as those tools, resulting in a lost opportunity to improve climate actions' knowledge transferability. Thus, we reviewed nine existing databases of adaptation and five mitigation databases, comprising a total of 7.130 adaptation actions and 11.409 mitigation actions, and detected a lack of alignment with climate planning practices and claims. Furthermore, we revealed a lack of coherency regarding the level of abstraction of climate actions and their role in the implementation process. Not all climate actions are meant to operate similarly from a planning perspective: while some had a direct outcome on the target indicators, others are thought to facilitate their implementation. Ultimately, we created a new integrated database of adaptation and mitigation measures in Europe, focusing exclusively on climate planning and implementation practices. First, we identified specific and transferable mitigation and adaptation measures and instruments through an originally designed decision tree. Second, we harmonised the collection of climate actions in a unique framework based on one of the biggest climate planning initiatives: the Sustainable and Energy Climate Action Plans by the Covenant of Mayors. Our integrated database of adaptation and mitigation measures (1) classifies and relates the different types of climate actions; (2) provides data that may improve the quality of climate plans and facilitate implementation; (3) allows a better perspective of systematic problems by identifying potential synergies and trade-offs; and (4) defines and characterises measures using a framework that draws on actual practice. The database compiles a total of 191 adaptation measures, 188 mitigation measures, and 97 measures that account for each, and a total of 609 associated instruments. For monitoring their outcomes, 93 SDG relevant indicators  are included.

ICARIA: climate projections from statistical downscaling outputs

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table>   The results shared here are developed over each of the observational locations that were retrieved to run the statistical downscaling. Both the observational datasets and the future climate change projections can be found here in a TXT format for each of the locations where they were developed. Observations include the main variables retrieved after a quality and homogeneity control, and climate projections together with extreme indicators include each of the 10 models, the 4 Tier 1 SSPs and data until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

Open AI Literature 2010-2020 Dataset

The OAIL_10-20 dataset is comprised of OpenAlex records which reproduce the majority of the Web of Science (WoS) records analysed in the course of writing the paper Patterns in the Growth and Thematic Evolution of Artificial Intelligence Research: A Study Using Bradford Distribution of Productivity and Path Analysis, Gupta et al. This paper aims to utilise the Bradford distribution to provide a focused analysis of the thematic evolution of research patterns and growth, and applies this analysis to a corpus of AI papers published over the 10 years between 2010 and 2020.  We provide this dataset to allow for researchers to reproduce the findings using open science.

Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy   Harim I. Won1,#, Samuel Zinga1,#, Olga Kandror1, Tatos Akopian1, Ian D. Wolf1, Jessica T.P. Schweber1, Ernst W. Schmid2, Michael C. Chao1, Maya Waldor1, Eric J. Rubin1,*, Junhao Zhu1,3,*   1Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA. 2Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA. 3CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China. #These authors contributed equally to this work. *Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)   Abstract Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in Mycobacterium smegmatis (Msm), a model for the human pathogen M. tuberculosis (Mtb). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential Msm proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future Mtb PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.

opencc-by-4.0Aug 2024View details →

Dataset of "From Gas to Solution: The Changing Neutral Structure of Proline Upon Solvation"

Liquid-jet photoelectron spectroscopy (LJ-PES) and electronic-structure theory were employed to investigate the chemical and structural properties of the amino acid L-proline in aqueous solution for its three ionized states (protonated, zwitterionic, deprotonated). This is the first PES study of this amino acid in its most biologically relevant environment. Proline's structure in the aqueous phase under neutral conditions is zwitterionic, distinctly different from the non-ionic neutral form in the gas phase. By analyzing the carbon 1s and nitrogen 1s core-levels as well as the valence spectra of aqueous-phase proline, we found that the electronic structure is dominated by the protonation state of each constituent molecular site (the carboxyl and amine) with small yet noticeable interference across the molecule. The site-specific nature of the core-level spectra enables probing of individual molecular constituents. The valence photoelectron spectra are more difficult to interpret because of overlapping signals of proline with the solvent and pH-adjusting agents (HCl and NaOH). Yet we are able to reveal subtle effects of specific (hydrogen-bonding) interaction with the solvent on the electronic structure. We also demonstrate that the relevant conformational space is much smaller for aqueous-phase proline than it is for its gas phase analogue. This study suggests that caution must be taken when comparing photoelectron spectra for gaseous and aqueous-phase molecules, particularly if those molecules are readily protonated / deprotonated in solution.

Collections (from American Folklife Center)

Dataset originally created 03/01/2019 UPDATE: Packaged on 04/18/2019 UPDATE: Edited README on 04/18/2019 I. About this Data Set This data set is a snapshot of work that is ongoing as a collaboration between Kluge Fellow in Digital Studies, Patrick Egan and an intern at the Library of Congress in the American Folklife Center. It contains a combination of metadata from various collections that contain audio recordings of Irish traditional music. The development of this dataset is iterative, and it integrates visualizations that follow the key principles of trust and approachability. The project, entitled, “Connections In Sound” invites you to use and re-use this data. The text available in the Items dataset is generated from multiple collections of audio material that were discovered at the American Folklife Center. Each instance of a performance was listed and “sets” or medleys of tunes or songs were split into distinct instances in order to allow machines to read each title separately (whilst still noting that they were part of a group of tunes). The work of the intern was then reviewed before publication, and cross-referenced with the tune index at www.irishtune.info. The Items dataset consists of just over 1000 rows, with new data being added daily in a separate file. The collections dataset contains at least 37 rows of collections that were located by a reference librarian at the American Folklife Center. This search was complemented by searches of the collections by the scholar both on the internet at https://catalog.loc.gov and by using card catalogs. Updates to these datasets will be announced and published as the project progresses. II. What’s included? This data set includes: The Items Dataset – a .CSV containing Media Note, OriginalFormat, On Website, Collection Ref, Missing In Duplication, Collection, Outside Link, Performer, Solo/multiple, Sub-item, type of tune, Tune, Position, Location, State, Date, Notes/Composer, Potential Linked Data, Instrument, Additional Notes, Tune Cleanup. This .CSV is the direct export of the Items Google Spreadsheet III. How Was It Created? These data were created by a Kluge Fellow in Digital Studies and an intern on this program over the course of three months. By listening, transcribing, reviewing, and tagging audio recordings, these scholars improve access and connect sounds in the American Folklife Collections by focusing on Irish traditional music. Once transcribed and tagged, information in these datasets is reviewed before publication. IV. Data Set Field Descriptions IV a) Collections dataset field descriptions ItemId – this is the identifier for the collection that was found at the AFC Viewed – if the collection has been viewed, or accessed in any way by the researchers. On LOC – whether or not there are audio recordings of this collection available on the Library of Congress website. On Other Website – if any of the recordings in this collection are available elsewhere on the internet Original Format – the format that was used during the creation of the recordings that were found within each collection Search – this indicates the type of search that was performed in order that resulted in locating recordings and collections within the AFC Collection – the official title for the collection as noted on the Library of Congress website State – The primary state where recordings from the collection were located Other States – The secondary states where recordings from the collection were located Era / Date – The decade or year associated with each collection Call Number – This is the official reference number that is used to locate the collections, both in the urls used on the Library website, and in the reference search for catalog cards (catalog cards can be searched at this address: https://memory.loc.gov/diglib/ihas/html/afccards/afccards-home.html) Finding Aid Online? – Whether or not a finding aid is available for this collection on the internet b) Items dataset field descriptions id – the specific identification of the instance of a tune, song or dance within the dataset Media Note – Any information that is included with the original format, such as identification, name of physical item, additional metadata written on the physical item Original Format – The physical format that was used when recording each specific performance. Note: this field is used in order to calculate the number of physical items that were created in each collection such as 32 wax cylinders. On Webste? – Whether or not each instance of a performance is available on the Library of Congress website Collection Ref – The official reference number of the collection Missing In Duplication – This column marks if parts of some recordings had been made available on other websites, but not all of the recordings were included in duplication (see recordings from Philadelphia Céilí Group on Villanova University website) Collection – The official title of the collection given by the American Folklife Center Outside Link – If recordings are available on other websites externally Performer – The name of the contributor(s) Solo/multiple – This field is used to calculate the amount of solo performers vs group performers in each collection Sub-item – In some cases, physical recordings contained extra details, the sub-item column was used to denote these details Type of item – This column describes each individual item type, as noted by performers and collectors Item – The item title, as noted by performers and collectors. If an item was not described, it was entered as “unidentified” Position – The position on the recording (in some cases during playback, audio cassette player counter markers were used) Location – Local address of the recording State – The state where the recording was made Date – The date that the recording was made Notes/Composer – The stated composer or source of the item recorded Potential Linked Data – If items may be linked to other recordings or data, this column was used to provide examples of potential relationships between them Instrument – The instrument(s) that was used during the performance Additional Notes – Notes about the process of capturing, transcribing and tagging recordings (for researcher and intern collaboration purposes) Tune Cleanup – This column was used to tidy each item so that it could be read by machines, but also so that spelling mistakes from the Item column could be corrected, and as an aid to preserving iterations of the editing process V. Rights statement The text in this data set was created by the researcher and intern and can be used in many different ways under creative commons with attribution. All contributions to Connections In Sound are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creators of these datasets. VI. Creator and Contributor Information Creator: Connections In Sound Contributors: Library of Congress Labs VII. Contact Information Please direct all questions and comments to Patrick Egan via www.twitter.com/drpatrickegan or via his website at www.patrickegan.org. You can also get in touch with the Library of Congress Labs team via LC-Labs@loc.gov.

WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine

<h1>1. General description </h1> This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine.  <h2>1.1 Summary of the shared structural information</h2> The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a> <h2>1.2 Summary of the shared geotechnical information</h2> Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016). The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., Undrained shear strength). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is: <ul> <li>"epsilon50 [-]" represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA).  <h2>1.3 Summary of the shared measurement data</h2> Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks.  The sensor network on NRT-WTG is illustrated in in Fig. 2, whereas a description of the sensor types is presented in Tab.1. The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall. <table> <tbody> <tr> <td>Data type </td> <td>Sensor type</td> <td>Fs (Hz)</td> <td> Level mLAT (m) </td> <td>Description </td> </tr> <tr> <td>Acceleration (g)  </td> <td>Piezo-electric acc. sensor (ACC)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (SG)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (FBG)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> Table 1. Description of sensor types. The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data.  The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset.   In conjunction to the sensor channels presented in Tab. 1, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in Tab 2. <table> <tbody> <tr> <td>Parameter</td> <td>Unit</td> <td>Description</td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> Table 2. List of provided SCADA parameters   A summary of the selected intervals and relevant corresponding scada parameters is given in Tab 3. <table> <tbody> <tr> <td>Scenario </td> <td>T1 (UTC)</td> <td>T2 (UTC) </td> <td>Windspeed</td> <td>RPM </td> <td>Pitch </td> </tr> <tr> <td>Parked</td> <td> 03/07  01:30 </td> <td> 03/07 03:30 </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> 05/07 22:30 </td> <td> 06/07 00:30  </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> Table 3. Selected data intervals and relevant scada parameters   <h1>2. Included in this version </h1> <h2>2.1 Version - 0.1.0</h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul>   <h1>3. Importing parquet files   </h1> To import the measurement data into Python it is recommended to use pandas: <pre>import pandas as pd # Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>

Quantitative Assessment of Research Data Management Practices - 2023

This survey investigates Research Data Management (RDM) practices across five Swiss higher education institutions, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on Active Data Management, particularly during research projects, including a range of topics such as: <ul> <li>Storage and backup solutions</li> <li>Data and code sharing platforms</li> <li>Documentation and metadata usage</li> <li>Compliance with legal and ethical standards</li> <li>Long-term data preservation strategies</li> <li>Use of open formats and open-source software</li> <li>Adoption of Data Management Plans (DMPs)</li> </ul> This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy. Included in this dataset: <ul> <li>A CSV and XLSX file with the aggregated, anonymized data from the survey.</li> <li>Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.</li> <li>A README file providing context.</li> </ul> This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.

opencc-by-4.0Mar 2024View details →

Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"

3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).

Dataset for training the Surrogate Model of microlaser neurons on the reduced MNIST classification task

This dataset was used to train a surrogate multilayer perceptron surrogate model of microlaser neurons. It is in csv format. It was generated using the Yamada Model as found in  Selmi F, Braive R, Beaudoin G, Sagnes I, Kuszelewicz R and Barbay S 2014 Relative Refractory Period in an Excitable Semiconductor Laser Phys. Rev. Lett. 112 183902.

opencc-by-4.0Jan 2024View details →

Mineral spectral refractive index and bulk optical property dataset for aerosol studies

Version 1.3, updated 11/15/2024. Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx', along with the refractive index data. All refractive index files here have 127 rows (wavelengths) and 27 columns (samples) 'kall27_coarse.dat' is the imaginary part of the coarse mode.  'kall27_fine.dat' is the imaginary part of the fine mode. 'nall27_coarse.dat' is the real part of the coarse mode. 'nall27_fine.dat' is the real part of the fine mode. Version 1.2, updated 04/23/2024. Major changes:  Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively. The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat". Added supplemental file data in 'Supplemental.tar.gz'. Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz' Version 1.1, updated 03/14/2024. Major changes:  Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)"; Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row. Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models. Version 1.0, uploaded 01/02/2024. This database include supplemental data and files for the publication of this paper: Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.   ***************************************** The supplemental data include: 1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files. E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k. The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron. The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations. The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples. 2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group). 'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite. 'Vlog_fine.dat': For the fine mode dust. 3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well. 4) 'DustSamples.tar.gz' Dust sample refractive index files. The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/. fine: fine mode. coarse: coarse mode. 'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt'). All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx'). The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part. 5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices) 'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca. 'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi). 'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat") 'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm). 6) 'PhaseMatrices.tar.gz' Phase matrices data 'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44. 'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave). There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data. 7) 'Supplemental.tar.gz' We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included. The supplemental bulk property files are named based on the figure number. 8) 'AdditionalRefInd.tar.gz' We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models. For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.

opencc-by-4.0Sep 2024View details →

Ground temperature time series in European mountain permafrost

RELATED PUBLICATION This dataset is related to the following publication: Noetzli J., Isaksen, K., Barnett, J., Chrisitiansen, H.H., Delaloye, R., Etzelmueller, B., Farinotti, D., Gallemann, T., Guglielmin, M., Hauck, C., Hilbich, C., Hoelzle, M., Lambiel, C., Magnin, F., Oliva, M., Paro, L, Pogliotti, P., Riedl, C., Schoeneich, P., M., Valt, M., Vieli A., Philliips, M. (2024). Enhanced permafrost warming in European mountains in the 21st century. Nature Communications, 15, 10508, <a href="https://doi.org/10.1038/s41467-024-54831-9">https://doi.org/10.1038/s41467-024-54831-9</a>. ==> For information on the measurements, selection criteria, processing information and data providers please refer to the methods, data availability and acknowledgements sections of the related publication !    --------------------------------------------------------------------------------------------------------------------------- CONTENT The dataset includes monthly and annual time series of ground temperatures measured in 64 boreholes in European mountain permafrost areas and corresponding metadata. Temporal coverage: at least 10 years until 2022 Spatial coverage: European mountain regions (Svalbard, Scandinavia, Iceland, European Alps, Sierra Nevada) Depth of measurements: at least 10 m; for all boreholes data of the sensors closest to 5, 10 and 20 m depth are included Monthly means are calculated from daily values and annual values are derived from monthly mean values.   --------------------------------------------------------------------------------------------------------------------------- DATA COMPILATION The data were compiled to derive 10-year and 20-year warming rates in European mountain permafrost in the study by Noetzli et al. (in review, see above). Data were collected from national permafrost observation networks as well as from individual institutions (e.g, universities, environmental agencies). The aquisition of long time series over decades requires long-term committment from the responsible institutions to maintain instruments and to collect and curate the data. Details on the data source for each time series can be found in the metadata file as well as in the related publication. The main data sources by country are given in the list below. <table> <tbody> <tr> <td>Country</td> <td>Data source (institution or national network)</td> </tr> <tr> <td>Austria</td> <td>GeoSphere Austria</td> </tr> <tr> <td>France</td> <td>Réseau français d'observation du permafrost (PermaFrance, <a href="https://wslch365-my.sharepoint.com/personal/jeannette_noetzli_slf_ch/Documents/PermafrostEurope/permafrance.osug.fr">permafrance.osug.fr</a>)</td> </tr> <tr> <td>Germany</td> <td>Bavarian Environment Agency</td> </tr> <tr> <td>Iceland</td> <td>University of Oslo</td> </tr> <tr> <td>Italy</td> <td>ARPA Piemonte, ARPA Valle d'Aosta, ARPA Veneto, University of Insubria</td> </tr> <tr> <td>Norway</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Spain</td> <td>Universitat de Barcelona</td> </tr> <tr> <td>Svalbard</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Sweden</td> <td>University of Stockholm</td> </tr> <tr> <td>Switzerland</td> <td>Swiss Permafrost Monitoring Network PERMOS (<a href="http://www.permos.ch">http://www.permos.ch</a>)</td> </tr> </tbody> </table>   --------------------------------------------------------------------------------------------------------------------------- FILES AND FORMAT This data set includes three csv-files: 1) metadata with information on the measurement location and data provider 2) monthly ground temperature time series and 3) annual ground temperature time series.  The variables in the three files are described below. Data files are in long data format. File 1 – borehole_overview.csv Key information on the boreholes, responsible institutions and contact persons. <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>Name</td> <td>Name of the borehole (as used in the related study)</td> </tr> <tr> <td>Country</td> <td>Alpha-2 code</td> </tr> <tr> <td>Region</td> <td>Larger region</td> </tr> <tr> <td>First_year</td> <td>First year of data</td> </tr> <tr> <td>Elevation [m asl.]</td> <td>Elevation of the borehole</td> </tr> <tr> <td>Lat [° N]</td> <td>Latitude</td> </tr> <tr> <td>Lon [° E]</td> <td>Longitude</td> </tr> <tr> <td>Depth [m]</td> <td>Total depth of the borehole</td> </tr> <tr> <td>DZAA [m]</td> <td>Depth of the Zero Annual Amplitude (uppermost sensor with annual amplitude ≤0.1)</td> </tr> <tr> <td>Phase lag</td> <td>Phase lag at 10 m depth compared to surface in months</td> </tr> <tr> <td>Morphology</td> <td>Main morphology of the site</td> </tr> <tr> <td>Surface_cover</td> <td>Main surface cover at the site</td> </tr> <tr> <td>Lithology</td> <td>Main lithology of the site</td> </tr> <tr> <td>Ice_content</td> <td>Basic classification by ground ice content at the site (no ice, ice-poor, ice-bearing, ice-rich), see publication for details</td> </tr> <tr> <td>Institution</td> <td>Responsible institution (in the year 2024)</td> </tr> <tr> <td>Contact_person</td> <td>Contact person (in the year 2024)</td> </tr> <tr> <td>Special_remarks</td> <td>Remarks on location, e.g. horizontal borehole</td> </tr> </tbody> </table>   File 2 – permafrost_temperatures_european_mountains_monthly_2022.csv Time series of monthly mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022. <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY-MM-DD]</td> <td>Date</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Monthly mean ground temperature (aggregated from daily values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum daily ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum daily ground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of daily values available to calculate monthly mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table>   File 3 – permafrost_temperatures_european_mountains_annual_2022.csv Time series of annual mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022. <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY]</td> <td>Year</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Annual mean ground temperature (aggregated from monthly values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum monthly ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum monthlyground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of monthly values available to calculate annual mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table>   --------------------------------------------------------------------------------------------------------------------------- CONTACT For question related to this dataset please contact the corresponding author: jeannette.noetzli@slf.ch.  For questions related to a specific time series, see metadata for contact information.

opencc-by-4.0Oct 2024View details →

A Dataset of sEMG and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis

Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow its identification and prevention. This dataset contains raw surface electromyographic (sEMG) data collected using the Delsys Trigno system, focusing on eight muscles, four per arm,  from 13 healthy adult participants. Participants performed a series of 12 upper-body dynamic movements, consisting of 4 uni-articular and 2 complex/compound movements per arm.  In addition to raw sEMG data, the dataset includes participants' self-reported fatigue levels.  Data Structure: <ul> <li>sEMG Data.zip: Recorded in 1259 Hz, formatted as .csv.</li> <li>self_perceived_fatigue_index.zip: Time-stamped fatigue ratings in 0-2 level, recorded at 50hz.</li> <li>Protocol: Includes trial description, movements illustration and sampling frequencies.</li> <li>Code: Jupyter Notebook file containing the base code to read and compute classic fatigue metrics such as Median Frequency and Mean Frequency.</li> <li>Metadata: Includes participant anthropometrics, exercise habits and caffeine intake on the day of the trials.</li> </ul> This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.

Prosopographia Memphitica

<h1>The Project</h1> The region of the ancient Egyptian residential city of Memphis is best known for its monumental pyramid complexes from the 3rd millennium BC. At the same time, the archaeological remains of the New Kingdom (1539–1077 BC) remained unexplored for a long time. Ongoing excavations at the burial grounds of the Egyptian elites in Saqqara and other sites within the Memphite region have revealed many inscribed objects and monuments. These artifacts, which belong to the material legacy of the royal residential city Memphis and its inhabitants, form the core of the research database presented here. They document a person’s name and institutional and genealogical affiliations and represent social identities within a complex network of persons. This project aimed to create a regional prosopography for the New Kingdom, focusing on all documented members of Memphite society. It seeks to identify individuals and shed light on their relationship patterns within a historical network of persons. The searchable web database results from the author’s dissertational project, Prosopographia Memphitica. Individuelle Identitäten und Kollektive Biographien einer königlichen Residenzstadt des Neuen Reiches at the Freie Universität Berlin, supervised by Univ.-Prof. Dr. Jochem Kahl (Freie Universität) and PD Dr. Dietrich Raue (Universität Leipzig) and funded by a Doctoral Fellowship at Excellence cluster TOPOI 264(Research Group C-1: Deixies and Frames of References: Strategies of Perspectivation inLanguage, Text, and Image.    <h1>The sources</h1> The archaeological remains of Memphis and its related necropoleis are located 18 km south of modern Cairo on today's west bank of the Nile. Although the scientific discussion about the site and its monuments forms an independent research focus, the archaeological legacy of the New Kingdom burial sites (1539–1077 BC) remained uninvestigated for a very long time. It was not until 1975 that the Egypt Exploration Society initiated the systematic documentation of tombs of the necropolis at Saqqara Memphite elite in cooperation with the Rijksmuseum van Oudheden, Leiden. Since then, several other fieldwork missions have been able to unearth further New Kingdom burial sites. As a result, the region known as Memphite Necropolis covers an area of about 40 sqm and reaches from Zawiyet el-Aryan in the North to Dahschur in the South. Although numerous inscribed objects of Memphite origin have found their way into various collections and museums around the world since the mid-19th century, and a wealth of epigraphic material continues to be unearthed regularly in the course of ongoing excavations, previous studies on the city of Memphis during the 2ndmillennium BC have mainly focused on gathering personal information about individuals or specific groups of persons such as the mayors of Memphis or the high priests of the local main deity Ptah. Therefore, our comprehension of Memphite society has been restricted to specific officials and institutions. Additionally, more than a small amount of data from these studies is needed for comprehensive analysis or digital processing.   <h1>First Steps</h1> To provide a representative prosopography, the compilation of prosopographical data has begun at the outset of the project by incorporating all data entries of a so far unpublished card box collection that Dietrich Raue has compiled since the 1990s; it comprised 1,121 individuals: 878 male and 243 female persons. Throughout the dissertation project duration (2015–2020), data was enriched with information from excavation reports, other relevant studies, and collection catalogs. Moreover, inscribed monuments of Memphite origin, housed in museum collections worldwide, have been thoroughly studied on-site and documented for the ProM-data collection. Due to collaboration with other museum collections, the author gained access to archival documents regarding acquisition history and photographic materials.   <h1>The Database</h1> To collect and archive as much data as possible on the Memphite region and its inhabitants, 1,700 objects and monuments from over 120 museums and collections worldwide, along with 2,409 individuals and 992 Egyptian functional titles documented on them, have been entered into a complex relational database using the PostgreSQL database management system.   This collection is the most extensive, free-accessible, object-based data collection for an ancient Egyptian site in the 2nd millennium BC. It serves as the primary reference work for prosopographic research on this period. As a result of a NfDI4Objects dataship (July-October 2024), the existing ProM dataset was revised according to the FAIR criteria for scientific research data management and subsequently made freely accessible to the scientific community. For this purpose, the following points were implemented as part of the N4O-dataship: 1. data enrichment 2. data consolidation 3. data documentation 4. data publication (Third Version) For more information, see: 10.5281/zenodo.13089803