cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/nebv-m189
title: Uniform expansion estimates in the quadratic map as a function of the partition size, computing λ only

description: This dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
Using an approach based on graph algorithms described in the paper, comprehensive computation was conducted to obtain a rigorous lower bound on the expansion exponent λ (Statement 1 in the paper), the constant C (Statement 1 in the paper), and the adjusted exponent λ0 (Statement 2 in the paper). The size of the partition into which the domain of the map was subdivided is denoted by K. The radius of the critical neighborhood is denoted by δ. The key parameter of the map is denoted by a. The parallelization framework introduced in the paper "Parallelization method for a continuous property" by P. Pilarczyk, as published in Foundations of Computational Mathematics, Vol. 10, No. 1 (2010), 93–114, doi: 10.1007/s10208-009-9050-8, was used in order to use several CPUs at a time. Note that the data records are not sorted.
In this specific computation, the quadratic map was analyzed with δ=0.01, a=2.0, and K∈[100,7900]. Only λ was computed in order to save time.
The data is provided in plain text format. Each line with comments begins with a semicolon, each line with a single data record begins with an asterisk. Each data record consists of the following items, separated with the space:
the asterisk that indicates the beginning of a data record
the identifier of the data record in the format level:number (e.g., 4:12)
the left endpoint of the parameter interval (minimal parameter value)
the right endpoint of the parameter interval (maximal parameter value)
the total number of intervals that cover both the critical neighborhood and the remainder of the domain of the map
the diameter δ of the critical neighborhood
the computed expansion exponent λ
the computed value of log C (0 if not computed)
the computed value of λ0 (0 if not computed)
the computation time (in seconds)
The actual software that was used to obtain the results and also some illustrations are available at http://www.pawelpilarczyk.com/unifexp/.
DOI: 10.34808/nq8v-4a13
title: The electrochemical response to Au electrode modification steps towards viral electrochemical immunosensor preparation

description: The dataset contains the electrochemical measurements (CV/EIS) carried out for the consecutive steps of the Au electrode functionalization, in separate subfolders, as described. The cyclic voltammetry (CV) was conducted using a Palmsens 4 potentiostat/galvanostat system (Methrom, Autolab, Netherlands) in the standard three-electrode configuration, with the Pt mesh was used as a counter electrode, and Ag/AgCl/0.1M KCl as a reference electrode. In the case of the EIS, the frequency ranged from 10 kHz to 0.2 Hz for Au electrode with 40 points. The amplitude of the alternating current (AC) signal was 10 mV. Each potential was held constant for 5 s before each measurement to obtain steady-state conditions. All electrochemical tests were carried out in 5 mM K3[Fe(CN)6]/K4[Fe(CN)6]/in 0.01 M PBS that was previously deaerated. The voltammetric behavior of the modified gold electrode in the presence of a simple redox system, K4[Fe(CN)6] in the supporting electrolyte of 0.1 M KCl, was compared with that of the bare electrode. 
The validation of immunosensors included the optimization of time needed for protein N to be detected, defining the limit of the detection, and cross-reactivity test with pathogens other than the target pathogen. The plots show detection of target pathogen the immunosensor based on Au electrode were prepared according to the presented procedure then the solution of protein N with a concentration of 266 μg/mL (positive sample) was diluted in 0.1 PBS was spotted on the surface of each electrode and incubated at a temperature of 5 °C for different periods. The last step of sensors validation was the cross-reactivity tests were performed with different pathogens and patients' saliva swabs sick with COVID-19 as positive samples. To prepare saliva swabs for the electrochemical measurement 1 mL of it was diluted in the 1 mL of electrolyte utilized in the whole experiment, but with twice time higher concentration of electrochemical species to obtain the same concentration of  5mM [Fe(CN)6]3-/4- after dilution. The 30 μL of that mixture was then spotted on the surface of each electrode and left for the incubation time estimated in previous measurements for each electrode material. In cross-reactivity tests the same sample of saliva swab was used for multiple samples of prepared sensors, ensuring better reproducibility and comparability of results. As pre-diagnosed infected swabs contained Streptococcus pyogenes (S.py) (Ct = 41), Streptococcus pneumonia (S.pn) (Ct = 43), and Hemophilus influenzae (Hi) (Ct = 45). 
These results are part of the manuscript published in Biosensors and Bioelectronics: DOI 10.1016/j.bios.2022.114222
DOI: 10.34808/p7dq-fq33
title: Uniform expansion estimates in the quadratic map as a function of the partition size, using the “critical” partition type

description: This dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
Using an approach based on graph algorithms described in the paper, comprehensive computation was conducted to obtain a rigorous lower bound on the expansion exponent λ (Statement 1 in the paper), the constant C (Statement 1 in the paper), and the adjusted exponent λ0 (Statement 2 in the paper). The size of the partition into which the domain of the map was subdivided is denoted by K. The radius of the critical neighborhood is denoted by δ. The key parameter of the map is denoted by a. The parallelization framework introduced in the paper "Parallelization method for a continuous property" by P. Pilarczyk, as published in Foundations of Computational Mathematics, Vol. 10, No. 1 (2010), 93–114, doi: 10.1007/s10208-009-9050-8, was used in order to use several CPUs at a time. Note that the data records are not sorted.
In this specific computation, the quadratic map was analyzed with δ=0.01, a=2.0, and K∈[100,7900]. Partition type called “critical” was used instead of the default uniform partition. This dataset was used in Figure 5 in the paper.
The data is provided in plain text format. Each line with comments begins with a semicolon, each line with a single data record begins with an asterisk. Each data record consists of the following items, separated with the space:
the asterisk that indicates the beginning of a data record
the identifier of the data record in the format level:number (e.g., 4:12)
the left endpoint of the parameter interval (minimal parameter value)
the right endpoint of the parameter interval (maximal parameter value)
the total number of intervals that cover both the critical neighborhood and the remainder of the domain of the map
the diameter δ of the critical neighborhood
the computed expansion exponent λ
the computed value of log C (0 if not computed)
the computed value of λ0 (0 if not computed)
the computation time (in seconds)
The actual software that was used to obtain the results and also some illustrations are available at http://www.pawelpilarczyk.com/unifexp/.
DOI: 10.5281/zenodo.18489835
title: A dataset of vertical profiles of O3, HONO and their contribution to OH radical from the hyperspectral vertical remote sensing network in China (2021-2024)

description: Photolysis of HONO and O3 in the troposphere is a primary source of OH radical and a fundamental control on atmospheric oxidative capacity. Their vertical distributions and diurnal evolution are therefore essential for elucidating photochemical processes in the planetary boundary layer and the lower free troposphere. Yet long-term, continuous observations of the vertical profiles of HONO, O3, their photolysis frequencies, and the resulting OH production rates remain extremely limited, particularly at multi-regional and interannual scales. Here we present vertical profile measurements of HONO and O3 acquired by the Chinese Hyperspectral Vertical Remote Sensing Network during 2021–2024. The dataset comprises 22 representative sites spanning urban, suburban, plateau, and basin environments, covering diverse surface and climatic regimes. Profiles extend from the surface to 4 km with ~100 m vertical resolution and ~15 min temporal resolution. Using the TUV model with co-retrieved aerosol and trace-gas profiles, we derive photolysis frequencies of HONO and O3 and the corresponding OH production rates, P(OH)HONO and P(OH)O3. The observations reveal robust regional patterns in the diurnal and vertical structure of tropospheric photochemical activity. Photolysis frequencies peak near local noon and generally increase with altitude from the surface layer to the upper mixed layer and the lower free troposphere, whereas OH production rates reach their maxima within the boundary layer and decrease with height. Processed using a unified retrieval framework and rigorous quality control, this dataset provides quantitative constraints on the contribution of HONO and O3 photolysis to tropospheric OH, supports improved radical parameterizations in chemical transport models, and enables synergistic multi-platform remote sensing analyses. By delivering the first systematic, long-term vertical profiles of HONO, O3, and their OH production in China, this public dataset fills a critical observational gap and offers a robust basis for investigating the spatiotemporal evolution of tropospheric oxidative capacity across regions and altitude ranges, with substantial scientific significance and long-term applicability.
description: When you use these data, please cite: "A dataset of vertical profiles of O3, HONO and their contribution to OH radical from the hyperspectral vertical remote sensing network in China (2021-2024)". and acknowledge the Atmospheric Composition Hyperspectral Vertical Remote Sensing Network established by Prof. Cheng Liu's research team at the University of Science and Technology of China.
DOI: 10.34808/8kpz-bd16
title: Conley-Morse graphs for a non-linear Leslie population model with 2 varying parameters

description: This dataset contains selected results of rigorous numerical computations conducted in the framework of the research described in the paper "A database schema for the analysis of global dynamics of multiparameter systems" by Z. Arai, W. Kalies, H. Kokubu, K. Mischaikow, H. Oka, P. Pilarczyk, published in SIAM Journal on Applied Dynamical Systems (SIADS), Vol. 8, No. 3 (2009), pp. 757–789, doi: 10.1137/080734935.
The parameter space [8,37]✕[3,50] was sampled at the resolution of 50✕50. The phase space was sampled at the resolution of 2048✕2048. A collection of isolating neighborhoods that enclose Morse sets in a Morse decomposition was computed for each box of parameters, and a Conley-Morse graph was determined, with the Conley indices of the Morse sets computed where feasible. Clutching graphs between Morse decompositions found for adjacent boxes were also computed, and the parameter space was subdivided into classes of equivalent Morse decompositions, as described in the paper. The parallelization framework introduced in the paper "Parallelization method for a continuous property" by P. Pilarczyk was used in the computations, as published in Foundations of Computational Mathematics, Vol. 10, No. 1 (2010), 93–114, doi: 10.1007/s10208-009-9050-8.
The dataset contains the Conley-Morse graphs computed for all the parameter boxes. Each graph is encoded in the text format compatible with the "dot" program from the Graphviz Graph Visualization Software package (https://graphviz.org/). All the text files are compressed together in the zipped archive file. The name of each file is in the format "gn_m.txt", where n,m are the integer coordinates of the box, both in the range [0,49]. For example, in order to obtain a PDF file with the visualization of the graph contained in the file "g12_0.txt" one could run the command "dot g12_0.txt -T pdf -o g12_0.pdf". Each node of the graph contains the information on the corresponding isolating neighborhood of a Morse set: the consecutive number of the neighborhood (starting with 0), the number of boxes comprising the neighborhood, and if a Conley index pair was constructed successfully then additionally the homology of the index pair, followed by the index map at each homology level, and the eigenvalues of the map at each homology level. The nodes have different shapes and colors for easy visual identification of the set type; for example, a yellow box indicates an attractor, and a cyan-filled oval stands for an isolating neighborhood with a non-trivial exit set. The directed edges between the nodes show direct connections between the sets (the transitive reduction should be taken for the full set of possible connections).
An interactive browser of all the Conley-Morse graphs and phase space portraits of the Morse decompositions provided in the current series of datasets is available at the address http://www.pawelpilarczyk.com/database/.
DOI: 10.34808/h38z-f324
title: Neural oscillations induced by IAPS pictures (session 24)

description: The data were collected to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals gathered from seven persons aged 23 - 35 within 26 sessions, during which 45 different random photos, taken from the International Affective Picture System – IAPS collection, were presented. All sessions were collected using the Emotiv Epoc+ device at the 256 Hz sampling rate, equipped with 14 electrodes arranged according to the 10-20 standard system.
The dataset contains three files:(1) session24_EEGLogger.csv,(2) session24_emotion_picture_timestamp.csv,(3) session24_power_brainwaves.csv.
The time-dependent (timestamp is in seconds) raw EEG signals (given in micro Volts) for all electrodes (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) are in the file (1). The timestamps (given in seconds) of displaying pictures, self-assessments of the emotional states (levels: sad, neutral, smiling) and the IAPS pictures ID are in the file (2). The display time of each picture is constant and equals 5 seconds. The low-resolution power of brainwaves (theta, alpha, low beta, high beta, and gamma) recorded for all electrodes during the session are in the file (3).
DOI: 10.34808/2ff7-je74
title: Uniform expansion estimates in the quadratic map as a function of the parameter, using the “uniform” partition type

description: This dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
Using an approach based on graph algorithms described in the paper, comprehensive computation was conducted to obtain a rigorous lower bound on the expansion exponent λ (Statement 1 in the paper), the constant C (Statement 1 in the paper), and the adjusted exponent λ0 (Statement 2 in the paper). The size of the partition into which the domain of the map was subdivided is denoted by K. The radius of the critical neighborhood is denoted by δ. The key parameter of the map is denoted by a. The parallelization framework introduced in the paper "Parallelization method for a continuous property" by P. Pilarczyk, as published in Foundations of Computational Mathematics, Vol. 10, No. 1 (2010), 93–114, doi: 10.1007/s10208-009-9050-8, was used in order to use several CPUs at a time. Note that the data records are not sorted.
In this specific computation, the quadratic map was analyzed with K=5000, δ=0.1, and a∈[1.7,2.0]. The uniform partition type was used.
The data is provided in plain text format. Each line with comments begins with a semicolon, each line with a single data record begins with an asterisk. Each data record consists of the following items, separated with the space:
the asterisk that indicates the beginning of a data record
the identifier of the data record in the format level:number (e.g., 4:12)
the left endpoint of the parameter interval (minimal parameter value)
the right endpoint of the parameter interval (maximal parameter value)
the total number of intervals that cover both the critical neighborhood and the remainder of the domain of the map
the diameter δ of the critical neighborhood
the computed expansion exponent λ
the computed value of log C (0 if not computed)
the computed value of λ0 (0 if not computed)
the computation time (in seconds)
The actual software that was used to obtain the results and also some illustrations are available at http://www.pawelpilarczyk.com/unifexp/.
DOI: 10.34808/40ek-kn84
title: Description of symmetrical prolate ellipsoid magnetic signature parameters-Be = 50 mT, I = 70 deg, z = 100 m, q = 80 deg, j = 135 deg, a =4 m, e = 1, mr = 100

description: The Earth magnetic field (Fig.1): BE – total magnetic flux density, BEx – x component of the Earth magnetic flux density, BEy = 0  y component of the Earth magnetic flux density, BEz – z component of the Earth magnetic flux density, I – inclination of the Earth magnetic field.
The symmetrical prolate ellipsoid (Fig.2): L – length of ellipsoid, a – radius of ellipsoid, e – unidimensional coefficient, q - vertical angle, j - horizontal angle.
For each scenarios there are following files:
Per_50%Mx_Bx_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]             Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]            By –  component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]             Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]               x coordinate [m]
Per_50%Mx _y_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]              y coordinate [m]
were:
BE [mT], I [deg], z [m], q [deg], j [deg], mr – relative magnetic permeability
Per_50%Mx – permanent magnetization of an ellipsoid in x direction is equal 50% induced magnetization for q = 90 deg and j = 0 deg
For the following parameters: Be = 50 mT, I = 70 deg, z = 100 m, q = 80 deg, j = 135 deg, a =4 m, e = 1, mr = 100
The names files are:
Per_50%Mx_Bx_50_70_100_80_135_4_1_100      Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_50_70_100_80_135_4_1_100       By – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_50_70_100_80_135_4_1_100       Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_50_70_100_80_135_4_1_100          x coordinate [m]
Per_50%Mx_y_50_70_100_80_135_4_1_100         y coordinate [m]
For the following parameters: Be = 50 mT, I = 70 deg, z = 10 m, q = 90 deg, j = -90 deg, a =4 m, e = 5, mr = 100
The names files are:
Per_50%Mx_Bx_50_70_10_90_m90_4_5_100  Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_50_70_10_90_m90_4_5_100  By – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_50_70_10_90_m90_4_5_100  Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_50_70_10_90_m90_4_5_100     x coordinate [m]
Per_50%Mx_y_50_70_10_90_m90_4_5_100     y coordinate [m]
DOI: 10.34808/89d9-n025
title: Description of symmetrical prolate ellipsoid magnetic signature parameters-Be = 50 mT, I = 70 deg, z = 10 m, q = 90 deg, j = 135 deg, a =4 m, e = 1, mr = 100

description: The Earth magnetic field (Fig.1): BE – total magnetic flux density, BEx – x component of the Earth magnetic flux density, BEy = 0  y component of the Earth magnetic flux density, BEz – z component of the Earth magnetic flux density, I – inclination of the Earth magnetic field.
The symmetrical prolate ellipsoid (Fig.2): L – length of ellipsoid, a – radius of ellipsoid, e – unidimensional coefficient, q - vertical angle, j - horizontal angle.
For each scenarios there are following files:
Per_50%Mx_Bx_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]             Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]            By –  component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]             Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]               x coordinate [m]
Per_50%Mx _y_[BE]_[I]_[z]_[q]_[j]_[a]_[e]_[mr]              y coordinate [m]
were:
BE [mT], I [deg], z [m], q [deg], j [deg], mr – relative magnetic permeability
Per_50%Mx – permanent magnetization of an ellipsoid in x direction is equal 50% induced magnetization for q = 90 deg and j = 0 deg
For the following parameters: Be = 50 mT, I = 70 deg, z = 10 m, q = 90 deg, j = 135 deg, a =4 m, e = 1, mr = 100
The names files are:
Per_50%Mx_Bx_50_70_10_90_135_4_1_100      Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_50_70_10_90_135_4_1_100       By – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_50_70_10_90_135_4_1_100       Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_50_70_10_90_135_4_1_100          x coordinate [m]
Per_50%Mx_y_50_70_10_90_135_4_1_100         y coordinate [m]
For the following parameters: Be = 50 mT, I = 70 deg, z = 10 m, q = 90 deg, j = -90 deg, a =4 m, e = 5, mr = 100
The names files are:
Per_50%Mx_Bx_50_70_10_90_m90_4_5_100  Bx – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_By_50_70_10_90_m90_4_5_100  By – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_Bz_50_70_10_90_m90_4_5_100  Bz – component of the ellipsoid magnetic flux density [nT]
Per_50%Mx_x_50_70_10_90_m90_4_5_100     x coordinate [m]
Per_50%Mx_y_50_70_10_90_m90_4_5_100     y coordinate [m]