cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/nm5gzmdsjh.1
title: Metabolites identified - Follicular fluid metabolomic analysis in women with Hashimoto's thyroiditis

description: Complete list of metabolites analyzed and their respective concentrations found in the follicular fluid carried out in the research entitled Follicular fluid metabolomic analysis in women with Hashimoto's thyroiditis
DOI: 10.17632/7hy8c9f466.1
title: Pingping Dong 

description: Raw data of gut flora
DOI: 10.17632/rm33z2xd2s.1
title: TCA cycle metabolites associated with adverse outcomes after acute coronary syndrome: Mediating effects of renal function

description: To examine relationships of tricarboxylic acid (TCA) cycle metabolites with risk of cardiovascular events and all-cause mortality in 309 patients with acute coronary syndrome (ACS), and determine whether these associations were modified by renal function. We found that higher plasma TCA cycle metabolites including isocitrate, aconitate and d/l-2-hydroxyglutarate were associated with risk of MACE and all-cause mortality in ACS patients. Furthermore, mediation analyses demonstrated that eGFR is a strong mediator of these associations.
DOI: 10.5281/zenodo.7388696
title: ICB_Limagne1

description: ICB_Limagne1 data object generated by ORCESTRA.
DOI: 10.5281/zenodo.7388695
title: ICB_Limagne1

description: ICB_Limagne1 data object generated by ORCESTRA.
DOI: 10.5281/zenodo.7458950
title: ICB_Limagne1

description: ICB_Limagne1 data object generated by ORCESTRA.
DOI: 10.5281/zenodo.7449175
title: Arctic cubed sphere configuration of MITgcm with 2-km resolution

description: This repository includes a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm,Marshall et al., 1997; MITgcm Group, 2017) with a horizontal resolution of 2 km, which is described in Hutter & Losch (2020). It is based on a regional Arctic configuration (Nguyen et al., 2012) of the MITgcm, which represents the Northern face of a global cubed sphere configuration. The number of vertical layers is reduced to 16, with the first 5 layers covering the uppermost 120 m to decrease the computational cost associated with the ocean model component. The Refined Topography dataset 2 (RTopo-2) (Schaffer and Timmermann, 2016) is used as bathymetry for the entire model domain. The lateral boundary conditions are taken from the globally optimized ECCO-2 simulations (Menemenlis et al., 2008). The configuration is designed to use the 3-hourly Japanese 55-year Reanalysis (JRA-55, Kobayashi et al., 2015) with a spatial resolution of 0.5625° for surface boundary conditions. The ocean temperature and salinity are initialized on 1 January, 1992, from the World Ocean Atlas 2005 (Locarnini et al., 2006; Antonov et al., 2006). The initial conditions for sea ice are taken from the Polar Science Center (Zhang et al., 2003). Ocean and sea ice parameterizations and parameters are directly taken from Nguyen et al. (2011), with the ice strength P=2.264×104Nm−2. The configuration uses the classical discrimination of two ice classes: thin and thick ice (Hibler, 1979). The momentum equations are solved by an iterative method and line successive relaxation (LSR) of the linearized equations following Zhang and Hibler (1997). In each time step (\(\Delta\)t=120 s), 10 nonlinear steps are made and the linear problem is iterated until an accuracy of 10e−5 is reached or 500 iterations are performed. With this configuration, simulations were run from 1 January 1992 to 31 December 2012. We also provide pickup files to restart the simulation in 2012. To reference this configuration please use the citation of this repository and reference to the paper describing the configuration: Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020.
DOI: 10.5281/zenodo.7449174
title: Arctic cubed sphere configuration of MITgcm with 2-km resolution

description: This repository includes a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm,Marshall et al., 1997; MITgcm Group, 2017) with a horizontal resolution of 2 km, which is described in Hutter & Losch (2020). It is based on a regional Arctic configuration (Nguyen et al., 2012) of the MITgcm, which represents the Northern face of a global cubed sphere configuration. The number of vertical layers is reduced to 16, with the first 5 layers covering the uppermost 120 m to decrease the computational cost associated with the ocean model component. The Refined Topography dataset 2 (RTopo-2) (Schaffer and Timmermann, 2016) is used as bathymetry for the entire model domain. The lateral boundary conditions are taken from the globally optimized ECCO-2 simulations (Menemenlis et al., 2008). The configuration is designed to use the 3-hourly Japanese 55-year Reanalysis (JRA-55, Kobayashi et al., 2015) with a spatial resolution of 0.5625° for surface boundary conditions. The ocean temperature and salinity are initialized on 1 January, 1992, from the World Ocean Atlas 2005 (Locarnini et al., 2006; Antonov et al., 2006). The initial conditions for sea ice are taken from the Polar Science Center (Zhang et al., 2003). Ocean and sea ice parameterizations and parameters are directly taken from Nguyen et al. (2011), with the ice strength P=2.264×104Nm−2. The configuration uses the classical discrimination of two ice classes: thin and thick ice (Hibler, 1979). The momentum equations are solved by an iterative method and line successive relaxation (LSR) of the linearized equations following Zhang and Hibler (1997). In each time step (\(\Delta\)t=120 s), 10 nonlinear steps are made and the linear problem is iterated until an accuracy of 10e−5 is reached or 500 iterations are performed. With this configuration, simulations were run from 1 January 1992 to 31 December 2012. We also provide pickup files to restart the simulation in 2012. To reference this configuration please use the citation of this repository and reference to the paper describing the configuration: Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020.
DOI: 10.17632/rwpcb5xzw2.1
title: Task Specific Exercise Training versus Graded Motor Imagery on Pain, Function, Knee Muscle Torque and Task Performance for Knee Osteoarthritis

description: Background: Individuals with knee osteoarthritis (OA) might not be comfortable with vigorous manual therapy approaches due to severe pain. Complementary therapies can be effective in such situations. Task specific exercise training (TSET) and graded motor imagery (GMI) are two such therapies which are less explored for their effectiveness in knee OA. So, the aim of this study was to determine and compare the effects of TSET and GMI on pain, function, task performance, mobility and muscle torque in knee OA.
Methods: Thirty-two individuals aged 40-65 years with grade 2 and above knee OA and pain intensity of > 4 on verbal scale were randomly assigned to TSET (n=16) and GMI (n=16) groups who received intervention thrice a week for 3 weeks. Pre (session 1) and post (session 9) outcome analysis was done for pain intensity by Numerical Pain Rating Scale, Knee injury and osteoarthritis outcome score for quality of life (QOL), three performance-based tests [30-second chair climb test, timed stair climb test and floor transfer test], peak torque and ratio for quadriceps and hamstrings using Isokinetic testing system and knee range of motion by goniometry. 
Results: Statistically significant difference was seen within both groups for all the outcomes (P<0.05). Both groups were equally effective for pain (P= 0.731), QOL (P=0.863) as well as all the objective based outcomes (performance-based tests, knee ROM, peak torque and ratio)
Conclusion: Both TSET and GMI are effective complementary therapy approaches to treat knee OA in terms of pain, function and task performance.
DOI: 10.17632/cchy42kcv2.1
title: Discovery, Verification, and Validation of Walnut Protein Marker Peptides Using LC-MS Approaches

description: A key requirement of liquid chromatography-mass spectrometry (LC-MS)-based allergenic food protein analysis methods is to use protein marker peptides with good analytical performances in LC-MS analysis of commercial processed foods. In this study, we developed a multistage walnut protein marker peptide selection strategy involving marker peptide discovery and verification and LC-MS validation of chemically equivalent stable isotope-labeled peptides. This strategy proposed three walnut protein marker peptides, including two new marker peptides. Our LC-MS-based walnut protein analysis method using the three stable isotope-labeled peptides showed acceptable linearity (R2 >0.99), matrix effects (coefficient of variation <±15%), sensitivity (limit of detection >0.3 pg/μL, limit of quantification >0.8 pg/μL), recovery (85.1–103.4%), accuracy, and precision (coefficient of variation <10%). In conclusion, our multistage marker peptide selection strategy effectively selects specific protein marker peptides for sensitive detection and absolute quantification of walnut proteins in LC-MS analysis of commercial processed foods.
DOI: 10.17632/jd38jhxpg6.1
title: sperm_move

description: Data used in "Predicting fertility from sperm motility landscapes".
DOI: 10.17632/jd38jhxpg6.2
title: sperm_move

description: Data and code (see related links) are intended to permit reproducibility of the results from Fernández-López et al, 2022. Note that all the elements are present to produce new results, and that these results should not match perfectly those presented in the paper (as all the methods are stochastic, and are not intended to yield identical results). For this reason, only raw data is provided. For comparative purposes, there is a "Results" folder in which you'll find the output from the models (coefficients and predictions).

For more details, check out the methods of "Predicting fertility from sperm motility landscapes" (Fernández-López et al., 2022). For any issues regarding data or code, please publish an issue in the corresponding github repository.

In the data folder you will find the raw data of fresh sperm motility and fertility data, and the joint dataset of capacitated and fresh sperm.

fertility_data.csv contains the information about insemination outcomes. Namely (from left to right columns): boar unique ID, boar's age, sow unique ID, sow parity (number of times said sow has completed a cycle of pregnancy, from insemination to delivery), sow's age, insemination date (in format DD/MM/YYYY), number of born pigglets in said insemination, number of dead born pigglets, number of alive born pigglets, seminal dose extraction date (in format DD/MM/YYYY), whether the insemination succeded (pregnancy, 1) or not (0) and the ejaculate unique ID.

sperm_data.RData (or .csv) contains the motility features of the seminal doses described in the fertility dataset. The data contains the unique identifier for the boars and their ejaculates, as well as several motility features of the sperm. In particular (from left to right columns): area of the head of the spermatozoon, curvilinear velocity, straight-line velocity, average path velocity, linearity, straightness, wobble, amplitude of lateral head displacement, beat-cross frequency, boar unique ID and ejaculate unique ID.

cap_motdata.RData (or .csv) contains the motility features of the combined fresh and capacitated sperm, that was used to generate the Figure 5 in "Predicting fertility from sperm motility landscapes" (Fernández-López et al., 2022).  This dataset only contains the main variables (VCL, VSL, ALH and BCF), as well as a label indicating whether the spermatozoon was incubated in capacitating conditions (Capacitated) or not (Fresh).

### RESULTS FOLDER ###

In the results folder you will find the posterior distributions of the coefficients of the three different models (Barnes-Hut t-SNE, FIt-SNE and UMAP), as well as the posterior distributions of the predicted fertility outcomes for the different boars. Each row corresponds to a draw from the posterior distribution, for each of the variables (columns), which are the sperm motility features, the intercept for each boars, and other parameters (see Fernández-López et al., 2022 for details).
DOI: 10.17632/jd38jhxpg6.3
title: Data used in Predicting fertility from sperm motility landscapes

description: Data for permit reproducibility of the results in Predicting fertility from sperm motility landscapes (Fernández-López et al., 2022). Check the related github repository (https://github.com/Polfe94/sperm_move) for instructions on how to reproduce the results from the paper. For more details, check out the methods section of the paper. For any issues regarding data or code, please publish an issue in the corresponding github repository.

Raw data files (in RAW_DATA folder): fertility_data.csv, sperm_data.csv and capacitated_dataset.csv. 

fertility_data.csv contains the information about insemination outcomes. Namely (from left to right columns): boar unique ID, boar's age, sow unique ID, sow parity (number of times said sow has completed a cycle of pregnancy, from insemination to delivery), sow's age, insemination date (in format DD/MM/YYYY), number of born pigglets in said insemination, number of dead born pigglets, number of alive born pigglets, seminal dose extraction date (in format DD/MM/YYYY), whether the insemination succeded (pregnancy, 1) or not (0) and the ejaculate unique ID.

sperm_data.csv contains the motility features of the seminal doses described in the fertility dataset. The data contains the unique identifier for the boars and their ejaculates, as well as several motility features of the sperm. In particular (from left to right columns): area of the head of the spermatozoon, curvilinear velocity, straight-line velocity, average path velocity, linearity, straightness, wobble, amplitude of lateral head displacement, beat-cross frequency, boar unique ID and ejaculate unique ID. The last 3 columns are the cluster labels, the merged cluster labels and the effect on motility of said clusters.

capacitated_dataset.csv contains the motility features of the combined fresh and capacitated sperm, that was used to generate the Figure 5 in "Predicting fertility from sperm motility landscapes" (Fernández-López et al., 2022).  This dataset only contains the main variables (VCL, VSL, ALH and BCF), as well as a label indicating whether the spermatozoon was incubated in capacitating conditions (Capacitated) or not (Fresh). The x and y columns are the corresponding coordinates in the landscape.

The other data files are related to the figures of the paper. Check the github repository for details (https://github.com/Polfe94/sperm_move).
DOI: 10.17632/jd38jhxpg6.4
title: Data used in Predicting fertility from sperm motility landscapes

description: Data for permit reproducibility of the results in Predicting fertility from sperm motility landscapes (Fernández-López et al., 2022). Check the related github repository (https://github.com/Polfe94/sperm_move) for instructions on how to reproduce the results from the paper. For more details, check out the methods section of the paper. For any issues regarding data or code, please publish an issue in the corresponding github repository.

Raw data files (in RAW_DATA folder): fertility_data.csv, sperm_data.csv and capacitated_dataset.csv. 

fertility_data.csv contains the information about insemination outcomes. Namely (from left to right columns): boar unique ID, boar's age, sow unique ID, sow parity (number of times said sow has completed a cycle of pregnancy, from insemination to delivery), sow's age, insemination date (in format DD/MM/YYYY), number of born pigglets in said insemination, number of dead born pigglets, number of alive born pigglets, seminal dose extraction date (in format DD/MM/YYYY), whether the insemination succeded (pregnancy, 1) or not (0) and the ejaculate unique ID.

sperm_data.csv contains the motility features of the seminal doses described in the fertility dataset. The data contains the unique identifier for the boars and their ejaculates, as well as several motility features of the sperm. In particular (from left to right columns): area of the head of the spermatozoon, curvilinear velocity, straight-line velocity, average path velocity, linearity, straightness, wobble, amplitude of lateral head displacement, beat-cross frequency, boar unique ID and ejaculate unique ID. The last 3 columns are the cluster labels, the merged cluster labels and the effect on motility of said clusters.

capacitated_dataset.csv contains the motility features of the combined fresh and capacitated sperm, that was used to generate the Figure 5 in "Predicting fertility from sperm motility landscapes" (Fernández-López et al., 2022).  This dataset only contains the main variables (VCL, VSL, ALH and BCF), as well as a label indicating whether the spermatozoon was incubated in capacitating conditions (Capacitated) or not (Fresh). The x and y columns are the corresponding coordinates in the landscape.

The other data files are related to the figures of the paper. Check the github repository for details (https://github.com/Polfe94/sperm_move).
DOI: 10.17632/jd38jhxpg6.5
title: Data used in Predicting fertility from sperm motility landscapes

description: Data for permit reproducibility of the results in Predicting fertility from sperm motility landscapes (https://doi.org/10.1038/s42003-022-03954-0). Check the related github repository (https://github.com/Polfe94/sperm_move) for instructions on how to reproduce the results from the paper. For more details, check out the methods section of the paper. For any issues regarding data or code, please publish an issue in the corresponding github repository.

Raw data files (in RAW_DATA folder): fertility_data.csv, sperm_data.csv and capacitated_dataset.csv. 

fertility_data.csv contains the information about insemination outcomes. Namely (from left to right columns): boar unique ID, boar's age, sow unique ID, sow parity (number of times said sow has completed a cycle of pregnancy, from insemination to delivery), sow's age, insemination date (in format DD/MM/YYYY), number of born pigglets in said insemination, number of dead born pigglets, number of alive born pigglets, seminal dose extraction date (in format DD/MM/YYYY), whether the insemination succeded (pregnancy, 1) or not (0) and the ejaculate unique ID.

sperm_data.csv contains the motility features of the seminal doses described in the fertility dataset. The data contains the unique identifier for the boars and their ejaculates, as well as several motility features of the sperm. In particular (from left to right columns): area of the head of the spermatozoon, curvilinear velocity, straight-line velocity, average path velocity, linearity, straightness, wobble, amplitude of lateral head displacement, beat-cross frequency, boar unique ID and ejaculate unique ID. The last 3 columns are the cluster labels, the merged cluster labels and the effect on motility of said clusters.

capacitated_dataset.csv contains the motility features of the combined fresh and capacitated sperm, that was used to generate the Figure 5 in "Predicting fertility from sperm motility landscapes" (Fernández-López et al., 2022).  This dataset only contains the main variables (VCL, VSL, ALH and BCF), as well as a label indicating whether the spermatozoon was incubated in capacitating conditions (Capacitated) or not (Fresh). The x and y columns are the corresponding coordinates in the landscape.

The other data files are related to the figures of the paper. Check the github repository for details (https://github.com/Polfe94/sperm_move).
DOI: 10.17632/btkxr7x6zy.1
title: LINF_340016700

description: uracil phosphoribosyltransferase
Leishmania infantum (strain JPCM5)
DOI: 10.17632/t7szkz8pdz.1
title: LINF_340015200

description: Wee1-like protein kinase
Leishmania infantum (strain JPCM5)
DOI: 10.17632/thkcz3h6n6.1
title: Olive Oil Fluorescence Spectra Dataset with chemical parameter values

description: This dataset encompasses fluorescence spectra of 24 olive oil samples from the 2019–2020 harvest provided by the producer Conde de Benalúa, Granada, Spain. The dataset consists in 10 Extra Virgin Olive Oil (EVOO), 8 Virgin Olive Oil (VOO) and 6 Lampante Olive Oil (LOO) samples. Fluorescence spectra were obtained with two excitation leds at 395 nm and 365 nm and the fluorescence signal was collected with a miniature spectrometer with 1024-element CCD with a resolution of 16 nm. For each olive oil sample and each led, 20 measurements were performed. A total of 24 samples × 40 measurements produced 960 spectra. The dataset, therefore, consists of 960 arrays, each having 1024 values whose elements are the intensity at the different pixel positions. For each spectra values for Acidity, Peroxide value, K_270, K_232, Ethyl Esters and the samples quality (EVOO, VOO or LOO). All the samples were analyzed by accredited laboratories for the chemical and organoleptic properties according to the current European regulation. The dataset can be used to train machine learning models to predict one or multiple chemical parameters or to classify samples based on their quality. There is no available datasets on fluorescence data on olive oil with the chemical parameters as described in this paper, thus making this dataset a very useful one for researchers in food technology.
DOI: 10.17632/thkcz3h6n6.2
title: Olive Oil Fluorescence Spectra Dataset with chemical parameter values

description: This dataset encompasses fluorescence spectra of 24 olive oil samples from the 2019–2020 harvest provided by the producer Conde de Benalúa, Granada, Spain. The dataset consists in 10 Extra Virgin Olive Oil (EVOO), 8 Virgin Olive Oil (VOO) and 6 Lampante Olive Oil (LOO) samples. Fluorescence spectra were obtained with two excitation leds at 395 nm and 365 nm and the fluorescence signal was collected with a miniature spectrometer with 1024-element CCD with a resolution of 16 nm. For each olive oil sample and each led, 20 measurements were performed. A total of 24 samples × 40 measurements produced 960 spectra. The dataset, therefore, consists of 960 arrays, each having 1024 values whose elements are the intensity at the different pixel positions. For each spectra values for Acidity, Peroxide value, K_270, K_232, Ethyl Esters and the samples quality (EVOO, VOO or LOO). All the samples were analyzed by accredited laboratories for the chemical and organoleptic properties according to the current European regulation. The dataset can be used to train machine learning models to predict one or multiple chemical parameters or to classify samples based on their quality. There is no available datasets on fluorescence data on olive oil with the chemical parameters as described in this paper, thus making this dataset a very useful one for researchers in food technology.