cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/w856-4b70
title: Ternary Perfect and Related Codes

description: This dataset keeps inequivalent collections of (from 1 to 9) ternary $(9,3^6,3)_3$ codes that are subsets o the all-parity-check $(9,3^6,3)_3$ code. The equivalence is understood in the sense of the automorphisms of the Hamming graph $H(9,3)$. There are 4 equivalence classes of such codes; 141 equivalence classes of pairs of disjoint codes; 10956 equivalence classes of triples; 118388 classes for 4 disjoint codes; 501915 for 5; 945965 for 6; 755066 for 7; 314833 for 8; 65436 equivalence classes of partitions of the all-parity-check $(9,3^6,3)_3$ code into 9 distance-3 codes. Such partitions, in combination with partitions of the Hamming space $H(4,3)$ into 9 1-perfect codes (the two inequivalent partitions of $H(4,3)$ can also be found in the file H43.py in this dataset), can be used to construct 1-perfect ternary codes of length 13 by concatenation, see [Romanov, A. M. On Non-Full-Rank Perfect Codes Over Finite Fields. Des. Codes Cryptography, 2019, 87, 995-1003]. The next goal of this project is to classify all concatenated 1-perfect $(13,3^{10},3)_3$ codes up to equivalence. The dataset will be updated.
DOI: 10.34740/kaggle/dsv/2321768
title: Dataset0x54
DOI: 10.5519/qd.olud8r65
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3163 records)
DOI: 10.5519/qd.58u5v0eq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (9528 records)
DOI: 10.34740/kaggle/dsv/2326730
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/2326732
title: Twitter: Trending Tweets per week (updated weekly)
DOI: 10.34740/kaggle/dsv/2326743
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.34740/kaggle/dsv/2326601
title: Credit Risk dataset
DOI: 10.6084/m9.figshare.14774649.v1
title: DRD2-sequencing.zip

description: The single nucleotide polymorphism (SNP) in the <i>DRD2</i> gene (<i>DRD2 C939T</i>) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.
DOI: 10.6084/m9.figshare.14774631.v1
title: LTA-sequence.zip

description: The single nucleotide polymorphism (SNP) in the <i>TNF-β </i>gene (<i>TNF-β +A252G</i> ) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.<br>
DOI: 10.6084/m9.figshare.14774655
title: Words and Gestalt Line Patterns

description: data are for Dprime and RT for two experiments with stimul consisting of words and line patterns superimposed. In Exp. 1, participants were instructed to pay attention to words in a word composite task.In Exp. 2, participants were instructed to pay attention to line patterns in a line pattern composite task.
DOI: 10.6084/m9.figshare.14774655.v1
title: Words and Gestalt Line Patterns

description: data are for Dprime and RT for two experiments with stimul consisting of words and line patterns superimposed. In Exp. 1, participants were instructed to pay attention to words in a word composite task.In Exp. 2, participants were instructed to pay attention to line patterns in a line pattern composite task.
DOI: 10.21227/hz0t-8482
title: 4DLFVD: A 4D Light Field Video Dataset

description: We present a 4D Light Field (LF) video dataset collected by the camera matrix, to be used for designing and testing algorithms and systems for LF video coding and processing. For collecting these videos, a 10x10 LF capture matrix composed of 100 cameras is designed and implemented, and the resolution of each camera is 1920x1056. The videos are taken in real and varying illumination conditions. The dataset contains a total of nine groups of LF videos, of which eight groups are collected with a fixed camera matrix position and a fixed orientation. The last group was collected by rotating around an outdoor specific target. Each group of LF videos consists of 100 video streams, which are encoded by H.265. Both static scenes (indoor potted plants and furniture, etc.) and dynamic scenes (roadside vehicles and pedestrians, etc.) are included, which can increase the diversity of our dataset. As a benchmark, we present the results of a depth estimation method designed by ourselves, and show that our dataset can be used for further processing and applications, such as objection detection and 3D modeling.
DOI: 10.34740/kaggle/dsv/2326887
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.14774667
title: DRD2.Genetic polymorphisms.xlsx

description: DRD2 C939T genotype distributions in migraine patients in A Northern Iranian population.
DOI: 10.6084/m9.figshare.14774631
title: LTA-sequence.zip

description: The single nucleotide polymorphism (SNP) in the <i>TNF-β </i>gene (<i>TNF-β +A252G</i> ) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.<br>
DOI: 10.6084/m9.figshare.14774667.v1
title: DRD2.Genetic polymorphisms.xlsx

description: DRD2 C939T genotype distributions in migraine patients in A Northern Iranian population.
DOI: 10.6084/m9.figshare.14774673
title: LTA.Genetic polymorphisms.xlsx

description: (TNF-β) +A252G genotype distributions in migraine patients in A Northern Iranian population.<br>
DOI: 10.6084/m9.figshare.14774673.v1
title: LTA.Genetic polymorphisms.xlsx

description: (TNF-β) +A252G genotype distributions in migraine patients in A Northern Iranian population.<br>
DOI: 10.6084/m9.figshare.14774649
title: DRD2-sequencing.zip

description: The single nucleotide polymorphism (SNP) in the <i>DRD2</i> gene (<i>DRD2 C939T</i>) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.
DOI: 10.6084/m9.figshare.14774649.v3
title: DRD2-sequencing.zip

description: The single nucleotide polymorphism (SNP) in the <i>DRD2</i> gene (<i>DRD2 C939T</i>) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.
DOI: 10.6084/m9.figshare.14774631.v3
title: LTA-sequence.zip

description: The single nucleotide polymorphism (SNP) in the <i>TNF-β </i>gene (<i>TNF-β +A252G</i> ) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.<br>
DOI: 10.6084/m9.figshare.14774649.v2
title: DRD2-sequencing.zip

description: The single nucleotide polymorphism (SNP) in the <i>DRD2</i> gene (<i>DRD2 C939T</i>) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.
DOI: 10.6084/m9.figshare.14774631.v2
title: LTA-sequence.zip

description: The single nucleotide polymorphism (SNP) in the <i>TNF-β </i>gene (<i>TNF-β +A252G</i> ) was genotyped by PCR restriction fragment length polymorphism (PCR-RFLP). Finally, to ensure the reliability of the PCR–RFLP results, the PCR products were sequenced by the Sanger sequencing method, and sequences of the PCR products were analyzed by the Codon Code Aligner software.<br>
DOI: 10.5281/zenodo.4936484
title: Estimating soil surface roughness with models based on the information about tillage practises and soil parameters - dataset

description: The data were collected on fields located in five settlements near Poznań, western Poland. The monitoring period of soil surface roughness included four seasons over a two-year period: early spring 2017, 2018, and late summer/early autumn 2017, 2018. Soil samples were also taken at this time to determine post-treatment soil moisture levels. Also, one-time soil samples were taken in the first year of research to determine physical and physicochemical soil properties. An explanation of the table column names: “field” - Abbreviation of the field where research was carried out in individual settlements. The total number of selected arable fields was 18. “subfield” - It is a marking of a specific place in the field where the research was conducted. Due to the variation of soil properties within individual fields, soil roughness was tested on selected test areas. “dd” – Day of data collection. “mm” - Month of data collection. “practise” - The type of tillage tool after which the soil surface roughness was recorded. “HSD” - Height Standard Deviation is a surface roughness index that was calculated from DEM (Digital Elevation Model) and expressed in millimeters. “T3D” - Tortuosity index is the second surface roughness index that was calculated from DEM. It expresses the ratio between the true surface of DEM and its flat horizontal area. “granulometric_group” - Name of the granulometric group to which soil samples were classified. Soil texture was determined by the hydrometer method according to standard PN-R-04032. “sand” - Sand content in the soil sample in %. “silt” – Silt content in the soil sample in %. “clay” – Clay content in the soil sample in %. “moisture” - The moisture level of the soil sample. Gravimetric water content was determined by the gravimetric method with oven drying. “p_hwater” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method. “p_hkcl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method. “SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization. “calcium_carbonate” – Calcium carbonate content in soil was obtained using the volumetric Scheibler method. “iron_oxide” –Iron oxide content in the soil.
DOI: 10.5281/zenodo.4936483
title: Estimating soil surface roughness with models based on the information about tillage practises and soil parameters - dataset

description: The data were collected on fields located in five settlements near Poznań, western Poland. The monitoring period of soil surface roughness included four seasons over a two-year period: early spring 2017, 2018, and late summer/early autumn 2017, 2018. Soil samples were also taken at this time to determine post-treatment soil moisture levels. Also, one-time soil samples were taken in the first year of research to determine physical and physicochemical soil properties. An explanation of the table column names: “field” - Abbreviation of the field where research was carried out in individual settlements. The total number of selected arable fields was 18. “subfield” - It is a marking of a specific place in the field where the research was conducted. Due to the variation of soil properties within individual fields, soil roughness was tested on selected test areas. “dd” – Day of data collection. “mm” - Month of data collection. “practise” - The type of tillage tool after which the soil surface roughness was recorded. “HSD” - Height Standard Deviation is a surface roughness index that was calculated from DEM (Digital Elevation Model) and expressed in millimeters. “T3D” - Tortuosity index is the second surface roughness index that was calculated from DEM. It expresses the ratio between the true surface of DEM and its flat horizontal area. “granulometric_group” - Name of the granulometric group to which soil samples were classified. Soil texture was determined by the hydrometer method according to standard PN-R-04032. “sand” - Sand content in the soil sample in %. “silt” – Silt content in the soil sample in %. “clay” – Clay content in the soil sample in %. “moisture” - The moisture level of the soil sample. Gravimetric water content was determined by the gravimetric method with oven drying. “p_hwater” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method. “p_hkcl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method. “SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization. “calcium_carbonate” – Calcium carbonate content in soil was obtained using the volumetric Scheibler method. “iron_oxide” –Iron oxide content in the soil.
DOI: 10.14272/pkxpspvtlwbidu-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3122 (w), 3094 (w), 3084 (w), 3080 (w), 3058 (w), 3030 (w), 3007 (w), 2986 (w), 2949 (w), 2925 (m), 2890 (w), 2849 (m), 2757 (w), 2731 (w), 2721 (w), 2714 (w), 2703 (w), 2696 (w), 2660 (w), 2643 (w), 2615 (w), 2592 (w), 2554 (w), 2546 (w), 2529 (w), 1680 (vs), 1609 (s), 1592 (w), 1499 (w), 1483 (w), 1453 (w), 1428 (m), 1412 (m), 1324 (m), 1313 (m), 1272 (w), 1231 (s), 1174 (w), 1162 (s), 1119 (m), 1094 (w), 1075 (w), 976 (m), 962 (w), 943 (m), 933 (w), 901 (vs), 881 (s), 846 (m), 824 (w), 796 (m), 745 (w), 710 (s), 676 (m), 653 (w), 637 (s), 618 (vs), 574 (w), 533 (w), 513 (vs), 472 (w), 458 (w), 424 (w), 402 (m) cm–1.
DOI: 10.14272/pkxpspvtlwbidu-uhfffaoysa-n/chmo0000480
title: electron ionisation mass spectrometry (EI-MS) ()
title: InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)

description: dataset for electron ionisation mass spectrometry (EI-MS) MS (EI, 70 eV, 100°C, %) m/z = 280 (37) [M+2H]+, 278 (35) [M]+, 173 (14) [M–C8H9]+, 105 (100) [C8H9]+.HRMS (EI, [M]+, C17H16O2) calcd.: 278.1307, found: 278.1308.
DOI: 10.14272/pkxpspvtlwbidu-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, MeOD, ppm) δ = 6.66 (dd, J = 7.9, 2.0 Hz, 1H, HAr), 6.61 (dd, J = 7.8, 1.9 Hz, 1H, HAr), 6.54 (ddd, J = 12.4, 7.7, 1.9 Hz, 2H, HAr), 6.50–6.45 (m, 3H, CH2, HAr), 6.42 (dd, J = 7.9, 1.9 Hz, 1H, HAr), 6.09 (d, J = 1.7 Hz, 1H, HAr), 3.14–3.03 (m, 3H, HPc), 3.03–2.99 (m, 1H, HPc), 2.99–2.89 (m, 4H, HPc).missing protons: ( 1H, CO2H)The spectrum contains 6% impurities.
DOI: 10.14272/pkxpspvtlwbidu-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, MeOD, ppm) δ = 171.2 (Cq, COOH), 144.5 (Cq, CAr), 140.8 (2C, Cq, CAr), 140.6 (Cq, CAr), 140.2 (Cq, CAr), 137.4 (Cq, CCH2), 135.9 (+, CH, CAr), 134.5 (+, CH, CAr), 134.4 (+, CH, CAr), 133.7 (+, CH, CAr), 133.0 (+, CH, CAr), 133.0 (+, CH, CAr), 129.6 (+, CH, CAr), 128.3 (–, CH2, CCH2), 36.2(–, CH2), 36.0(–, CH2), 35.8(–, CH2), 35.5(–, CH2).The spectrum contains 6% impurities according to 1H NMR
DOI: 10.14272/pkxpspvtlwbidu-uhfffaoysa-n.1
title: C19H18O2
title: InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)

description: This dataset contains experimental data from a sample (InChI=1S/C19H18O2/c1-13(19(20)21)18-12-16-7-6-14-2-4-15(5-3-14)8-10-17(18)11-9-16/h2-5,9,11-12H,1,6-8,10H2,(H,20,21)). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) electron ionisation mass spectrometry (EI-MS) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.25375/uct.14774706
title: Simulated genetic efficacy of metapopulation management and the conservation value of captive reintroductions in a rapidly declining felid

description: In South Africa, cheetah (<i>Acinonyx jubatus</i>) occur as a relictual, unmanaged population of ‘free-roamers’, a managed metapopulation across fenced reserves, and in various captive facilities. To ensure that the Cheetah Metapopulation Project (CMP) is not at risk of losing overall genetic variation to drift or inbreeding, we propose various interventions, including exchanges between free-roamers and the metapopulation or supplementation with unrelated individuals from captivity. Simulated trajectories of genetic diversity under such intervention strategies over time could directly inform conservation action and policy towards securing the long-term genetic integrity of the CMP. Single Nucleotide Polymorphisms (SNPs) were genotyped for 172 adult cheetah across the free-roamer population, the metapopulation, and three major captive facilities. Management intervention trajectory models were tested including, 1) no intervention, 2) genetic exchange between free-roamers and the metapopulation, 3) translocation from a single captive facility and 4) translocation from several captive facilities into the metapopulation. Discriminant Analysis of Principal Components (DAPC) showed that the two captive populations are highly differentiated from the metapopulation and each other, while the third captive and free-roamer populations are genetically more similar to the metapopulation. Simulated genetic variation over 25 generations indicated that models 1 and 2 show significant losses of heterozygosity due to genetic drift and present a proportional increase in the frequencies of first- and second-degree relatives, while this variation and pairwise relatedness remain relatively constant under models 3 and 4. We emphasise the potential importance of captive facilities as reservoirs of genetic diversity in metapopulation management and threatened species recovery.
DOI: 10.25375/uct.14774706.v1
title: Simulated genetic efficacy of metapopulation management and the conservation value of captive reintroductions in a rapidly declining felid

description: In South Africa, cheetah (<i>Acinonyx jubatus</i>) occur as a relictual, unmanaged population of ‘free-roamers’, a managed metapopulation across fenced reserves, and in various captive facilities. To ensure that the Cheetah Metapopulation Project (CMP) is not at risk of losing overall genetic variation to drift or inbreeding, we propose various interventions, including exchanges between free-roamers and the metapopulation or supplementation with unrelated individuals from captivity. Simulated trajectories of genetic diversity under such intervention strategies over time could directly inform conservation action and policy towards securing the long-term genetic integrity of the CMP. Single Nucleotide Polymorphisms (SNPs) were genotyped for 172 adult cheetah across the free-roamer population, the metapopulation, and three major captive facilities. Management intervention trajectory models were tested including, 1) no intervention, 2) genetic exchange between free-roamers and the metapopulation, 3) translocation from a single captive facility and 4) translocation from several captive facilities into the metapopulation. Discriminant Analysis of Principal Components (DAPC) showed that the two captive populations are highly differentiated from the metapopulation and each other, while the third captive and free-roamer populations are genetically more similar to the metapopulation. Simulated genetic variation over 25 generations indicated that models 1 and 2 show significant losses of heterozygosity due to genetic drift and present a proportional increase in the frequencies of first- and second-degree relatives, while this variation and pairwise relatedness remain relatively constant under models 3 and 4. We emphasise the potential importance of captive facilities as reservoirs of genetic diversity in metapopulation management and threatened species recovery.
DOI: 10.6084/m9.figshare.14774718
title: MiniSun

description: The Minisun dataset contains 100 classes randomly chosen from Sun397 with 100 images of size 84×84 pixels per class. It is split into 64 base classes, 16 validation classes, and 20 novel classes.
DOI: 10.6084/m9.figshare.14774718.v1
title: MiniSun

description: The Minisun dataset contains 100 classes randomly chosen from Sun397 with 100 images of size 84×84 pixels per class. It is split into 64 base classes, 16 validation classes, and 20 novel classes.
DOI: 10.5281/zenodo.4936573
title: Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: implementation and examples

description: This repository contains the Python codes and datasets used for the research described in the following paper: https://doi.org/10.5194/amt-2020-370 It has been prepared as supplementary information to the accepted/ final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors.
DOI: 10.5281/zenodo.4936572
title: Supplementary Information to: Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high–resolution visible channel: implementation and examples

description: This repository contains the Python codes and datasets used for the research described in the following paper: https://doi.org/10.5194/amt-2020-370 It has been prepared as supplementary information to the accepted/ final paper.<br> <br> Note that the actual Cloud Property Retrieval (CPP) which would be<br> required for full reproducability of the results cannot be made<br> available by the authors.
DOI: 10.5517/cc74f1t
title: CCDC 212785: Experimental Crystal Structure Determination

description: Related Article: J.Gonzalez-Platas, E.M.de la Rosa, F.G.Manrique, P.E.Ferrera, P.Martn-Zarza|2007|CSD Communication|||
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DOI: 10.5517/ccdc.csd.cc25fvj0
title: CCDC 2009312: Experimental Crystal Structure Determination

description: Related Article: Vitaly А. Podshibyakin, Еvgenii N. Shepelenko, Olga Yu. Karlutova, Lyudmila G. Kuzmina, Alexander D. Dubonosov, Vladimir A. Bren, Vladimir I. Minkin|2021|J.Mol.Struct.|1243|130758|doi:10.1016/j.molstruc.2021.130758
DOI: 10.5517/ccdc.csd.cc25t54b
title: CCDC 2020181: Experimental Crystal Structure Determination

description: Related Article: C. Shiju, D. Arish, N. Bhuvanesh, S. Kumaresan|2021|Polyhedron|205|115292|doi:10.1016/j.poly.2021.115292
DOI: 10.5517/ccdc.csd.cc25y4lw
title: CCDC 2024008: Experimental Crystal Structure Determination

description: Related Article: Ross F. Koby, Alicia M. Doerr, Nicholas R. Rightmire, Nathan D. Schley, William W. Brennessel, Brian K. Long, Timothy P. Hanusa|2021|Chem.-Eur.J.|27|8195|doi:10.1002/chem.202100589
DOI: 10.5517/ccdc.csd.cc25y4mx
title: CCDC 2024009: Experimental Crystal Structure Determination

description: Related Article: Ross F. Koby, Alicia M. Doerr, Nicholas R. Rightmire, Nathan D. Schley, William W. Brennessel, Brian K. Long, Timothy P. Hanusa|2021|Chem.-Eur.J.|27|8195|doi:10.1002/chem.202100589