cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13279761
title: Additional file 15 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 15. Ancestral state reconstructions of cynipoid life histories using a seven-state model. Reconstructions were performed under the ER-model using the rayDISC function in the R-package corHMM. Summarized here are ancestral state probabilities (ranging from 0–1) from 8 variations of analyses on the seven-state model, with states 0 = parasitoid, 1 = inquiline and 2 = galler-Fagaceae, 3 = galler-herbs, 4 = galler-Acer, 5 = galler-Rosaceae, 6 = galler-Acacia. Variations are a-i = Outgroups included, Paraulacini are coded as parasitoids, uncertain taxa coded as parasitoids; a-ii = Outgroups included, Paraulacini are coded as inquilines, uncertain taxa coded as parasitoids; b-i = Outgroups excluded, Paraulacini are coded as parasitoids, uncertain taxa coded as parasitoids; b-ii = Outgroups excluded, Paraulacini are coded as inquilines, uncertain taxa coded as parasitoids; c-i = Outgroups included, Paraulacini are coded as parasitoids, uncertain taxa coded as unknown; c-ii = Outgroups included, Paraulacini are coded as inquilines, uncertain taxa coded as unknowns; d-i = Outgroups excluded, Paraulacini are coded as parasitoids, uncertain taxa coded as unknowns; d-ii = Outgroups excluded, Paraulacini are coded as inquilines, uncertain taxa coded as unknowns. For each node, the state with the highest likelihood has been bolded. Grey shaded fields highlight nodes with conflict or variable support between the variations of analyses.
DOI: 10.6084/m9.figshare.13279767
title: Additional file 2 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 2. Library preparation and UCE sequence capture statistics. Table listing for each taxon included in the study the pre-library preparation DNA concentration, the total DNA input, the post-library preparation DNA concentration, the total raw read count, the total number of assembled contigs and mean length, and the number of assembled UCE contigs and their mean length, the number of UCE loci, and average GC content.
DOI: 10.6084/m9.figshare.13279770
title: Additional file 3 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 3. Alignment statistics. Various alignment statistics calculated with AMAS (Borowiec 2016).
DOI: 10.6084/m9.figshare.13279770.v1
title: Additional file 3 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 3. Alignment statistics. Various alignment statistics calculated with AMAS (Borowiec 2016).
DOI: 10.6084/m9.figshare.13279773.v1
title: Additional file 4 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 4. BINs defined for GC content experiments. Table listing average, minimum and maximum GC content across loci, topology estimated using unpartitioned ML analysis (for a sketch of topology A,B,C refer to Additional File 12), and support for the position of E. niger.
DOI: 10.6084/m9.figshare.13279764
title: Additional file 1 of Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps

description: Additional file 1. Information on specimen vouchers and collection data, as well as traits. Table listing USNMENT voucher numbers for newly sequenced taxa, collection data as available, and character states for the life history analyses. Character states are: 3-state model: 0 = parasitoids, 1 = inquilines, 2 = gallers; 7-state model: 0 = parasitoids, 1 = inquilines, 2 = gallers-Fagaceae, 3 = gallers-herbs, 4 = gallers-Acer, 5 = gallers-Rosaceae, 6 = gallers-Acacia; taxa with unspecified states were not included in dating and ancestral reconstructions; taxa with two states indicated were coded both ways in two separate analyses. n/a = not applicable, n.a. = not available; Biogeographic regions are: AFR = Afrotropical, AUS = Australasian, NEA = Nearctic, NEO = Neotropical, ORI = Oriental/Indomalayan, PAL = Palearctic.
DOI: 10.6084/m9.figshare.13279821
title: Additional file 1 of Praziquantel treatment coverage among school age children against Schistosomiasis and associated factors in Ethiopia: a cross-sectional survey, 2019

description: Additional file 1.
DOI: 10.6084/m9.figshare.13279815
title: Additional file 1 of A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter

description: Supplementary information (ZIP 1 kB)
DOI: 10.6084/m9.figshare.13279815.v1
title: Additional file 1 of A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter

description: Supplementary information (ZIP 1 kB)
DOI: 10.6084/m9.figshare.13279824.v1
title: Additional file 2 of Praziquantel treatment coverage among school age children against Schistosomiasis and associated factors in Ethiopia: a cross-sectional survey, 2019

description: Additional file 2.
DOI: 10.6084/m9.figshare.13279824
title: Additional file 2 of Praziquantel treatment coverage among school age children against Schistosomiasis and associated factors in Ethiopia: a cross-sectional survey, 2019

description: Additional file 2.
DOI: 10.6084/m9.figshare.13279821.v1
title: Additional file 1 of Praziquantel treatment coverage among school age children against Schistosomiasis and associated factors in Ethiopia: a cross-sectional survey, 2019

description: Additional file 1.
DOI: 10.6084/m9.figshare.13279893.v1
title: Additional file 1 of Global research trends at the intersection of coronavirus disease 2019 (COVID-19) and traditional, integrative, and complementary and alternative medicine: a bibliometric analysis

description: Additional file 1. Supplementary file 1. Characteristics of All Eligible Articles Published at the Intersection of COVID-19 and TICAM.
DOI: 10.6084/m9.figshare.13279893
title: Additional file 1 of Global research trends at the intersection of coronavirus disease 2019 (COVID-19) and traditional, integrative, and complementary and alternative medicine: a bibliometric analysis

description: Additional file 1. Supplementary file 1. Characteristics of All Eligible Articles Published at the Intersection of COVID-19 and TICAM.
DOI: 10.21227/v949-yf05
title: Gaussian Blobs of Varying numbers of samples, centers and features

description: The dataset has Gaussian Blobs of varying samples, centers and features. The number of samples ranges from 500 to 50,000. Similarly, the number of centers varies from 2 to 100, while the number of features varies from 2 to 2048. These different sets of Gaussian blobs can be used for testing clustering algorithms for their scalability and effectiveness. There are two kinds of files inside the compressed sets. Files ending with "_X.csv" consist of datapoints, while the files ending with "_y.csv" represent respective class data.The filename of each gaussian blob inside compressed sets gives a sketch of the blob. For example, the file "s50000_c50_f2048_X.csv" contains 50,000 samples of data that have 2048 dimensions (features) with 50 centers, and the file "s50000_c50_f2048_y.csv" is the associated class data of the file "s50000_c50_f2048_X.csv". The blob files are organized based on their number of samples. For example, the compressed file "10,000 datapoints set.zip" contains a collection of Gaussian blobs with 10,000 samples of data with a varying number of centers and features. The documentation section has PDF document that provides list of files inside each compressed file.The naming convention of the files uses following alphabets that represent the content of the repective file. s represents number of samplesc represents number of centersf represents number of features
DOI: 10.6084/m9.figshare.13279911
title: Additional file 2 of Illuminating the complexities of conflict with evolution: validation of the scales of evolutionary conflict measure (SECM)

description: Additional file 2: Table S3. Variance-covariance matrix.
DOI: 10.6084/m9.figshare.13279911.v1
title: Additional file 2 of Illuminating the complexities of conflict with evolution: validation of the scales of evolutionary conflict measure (SECM)

description: Additional file 2: Table S3. Variance-covariance matrix.
DOI: 10.6084/m9.figshare.13279914
title: Additional file 3 of Illuminating the complexities of conflict with evolution: validation of the scales of evolutionary conflict measure (SECM)

description: Additional file 3: Table S4. Means and standard deviations of raw scores.
DOI: 10.6084/m9.figshare.13279914.v1
title: Additional file 3 of Illuminating the complexities of conflict with evolution: validation of the scales of evolutionary conflict measure (SECM)

description: Additional file 3: Table S4. Means and standard deviations of raw scores.
DOI: 10.5281/zenodo.4288132
title: Supplementary

description: Supplementary
DOI: 10.5281/zenodo.4288131
title: Supplementary

description: Supplementary
DOI: 10.6084/m9.figshare.13280109
title: Electronic supplementary material file 2 from Expression levels of the tetratricopeptide repeat protein gene ttc39b covary with carotenoid-based skin colour in cichlid fish

description: Supplementary information on quantitative PCR primers and qPCR gene expression data
DOI: 10.6084/m9.figshare.13280109.v1
title: Electronic supplementary material file 2 from Expression levels of the tetratricopeptide repeat protein gene ttc39b covary with carotenoid-based skin colour in cichlid fish

description: Supplementary information on quantitative PCR primers and qPCR gene expression data
DOI: 10.21227/fsc4-kg97
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153
DOI: 10.21227/5m9c-mw89
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153
DOI: 10.21227/ehz3-vx34
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153
DOI: 10.6084/m9.figshare.13280111
title: Supplementary Material for: Akut suizidal-affektive Störung: Ein systematisches Review

description: <b><i>Hintergrund:</i></b> Die Arbeitsgruppe um Thomas Joiner postuliert ein als <i>acute suicidal affective disturbance</i> (ASAD) bezeichnetes Syndrom, welches durch vier Symptomgruppen (Suizidabsicht, Entfremdung, Hoffnungslosigkeit, Übererregung) definiert sein soll. Ziel des vorliegenden Artikels ist, die Literatur zum ASAD-Syndrom zusammenfassend darzustellen und eine Einschätzung vorzunehmen, inwieweit tatsächlich von einem einheitlichen Syndrom ausgegangen werden kann. <b><i>Methoden:</i></b> Im Rahmen einer Literaturrecherche konnten neun Artikel identifiziert werden, die im Zeitraum von 2016 bis 2020 zum Thema publiziert wurden. <b><i>Ergebnisse und Schlussfolgerungen:</i></b> Die Befundlage unterstützt den einheitlichen Störungscharakter und die Ab­grenzbarkeit der akut suizidal-affektiven Störung von anderen Störungsbildern. Die Aussagekraft der Befundlage ist dadurch eingeschränkt, dass bislang ausschließlich Querschnittsuntersuchungen durchgeführt wurden und keinerlei Befunde dazu vorliegen, ob ASAD tatsächlich suizidalem Verhalten vorausgeht.
DOI: 10.6084/m9.figshare.13200581
title: R scripts of performed analyses from Use of nursery areas by the extinct megatooth shark <i>Otodus megalodon</i> (Chondrichthyes: Lamniformes)

description: Nursery areas are fundamental for the success of many marine species, particularly for large, slow-growing taxa with low fecundity and high age of maturity. Here, we examine the population size-class structure of the extinct gigantic shark <i>Otodus megalodon</i> in a newly described middle Miocene locality from Northeastern Spain, as well as in eight previously known formations (Temblor, Calvert, Pisco, Gatun, Chucunaque, Bahía Inglesa, Yorktown and Bone Valley). In all cases, body lengths of all individuals were inferred from dental parameters and the size-class structure was estimated from Kernel probability density functions and Gaussian mixture models. Our analyses support the presence of five potential nurseries ranging from the Langhian (middle Miocene) to the Zanclean (Pliocene), with higher densities of individuals with estimated body lengths within the typical range of neonates and young juveniles. These results reveal, for the first time, that nursery areas were commonly used by <i>O. megalodon</i> over large temporal and spatial scales, reducing early mortality and playing a key role in maintaining viable adult populations. Ultimately, the presumed reliance of <i>O. megalodon</i> on the presence of suitable nursery grounds might have also been determinant in the demise of this iconic top predatory shark.
DOI: 10.6084/m9.figshare.13200581.v2
title: R scripts of performed analyses from Use of nursery areas by the extinct megatooth shark <i>Otodus megalodon</i> (Chondrichthyes: Lamniformes)

description: Nursery areas are fundamental for the success of many marine species, particularly for large, slow-growing taxa with low fecundity and high age of maturity. Here, we examine the population size-class structure of the extinct gigantic shark <i>Otodus megalodon</i> in a newly described middle Miocene locality from Northeastern Spain, as well as in eight previously known formations (Temblor, Calvert, Pisco, Gatun, Chucunaque, Bahía Inglesa, Yorktown and Bone Valley). In all cases, body lengths of all individuals were inferred from dental parameters and the size-class structure was estimated from Kernel probability density functions and Gaussian mixture models. Our analyses support the presence of five potential nurseries ranging from the Langhian (middle Miocene) to the Zanclean (Pliocene), with higher densities of individuals with estimated body lengths within the typical range of neonates and young juveniles. These results reveal, for the first time, that nursery areas were commonly used by <i>O. megalodon</i> over large temporal and spatial scales, reducing early mortality and playing a key role in maintaining viable adult populations. Ultimately, the presumed reliance of <i>O. megalodon</i> on the presence of suitable nursery grounds might have also been determinant in the demise of this iconic top predatory shark.
DOI: 10.6084/m9.figshare.13200581.v1
title: R scripts of performed analyses from Use of nursery areas by the extinct megatooth shark <i>Otodus megalodon</i> (Chondrichthyes: Lamniformes)

description: Nursery areas are fundamental for the success of many marine species, particularly for large, slow-growing taxa with low fecundity and high age of maturity. Here, we examine the population size-class structure of the extinct gigantic shark <i>Otodus megalodon</i> in a newly described middle Miocene locality from Northeastern Spain, as well as in eight previously known formations (Temblor, Calvert, Pisco, Gatun, Chucunaque, Bahía Inglesa, Yorktown and Bone Valley). In all cases, body lengths of all individuals were inferred from dental parameters and the size-class structure was estimated from Kernel probability density functions and Gaussian mixture models. Our analyses support the presence of five potential nurseries ranging from the Langhian (middle Miocene) to the Zanclean (Pliocene), with higher densities of individuals with estimated body lengths within the typical range of neonates and young juveniles. These results reveal, for the first time, that nursery areas were commonly used by <i>O. megalodon</i> over large temporal and spatial scales, reducing early mortality and playing a key role in maintaining viable adult populations. Ultimately, the presumed reliance of <i>O. megalodon</i> on the presence of suitable nursery grounds might have also been determinant in the demise of this iconic top predatory shark.
DOI: 10.6084/m9.figshare.13280093
title: Phenocam data from El Mogor, Baja California, Mexico, 2017

description: This dataset provides half-hourly observations of greenness index (I<sub>g</sub>), from one wildlife camera (M-550, Moultrie) for the year of 2017, at the study site of El Mogor, Baja California, Mexico (32.03017 N, 116.604219, 406 m asl). Vegetation at El Mogor is a mixture of chaparral and less-sclerophyllous species, including <i>Ornithostaphylos oppositifolia</i>, <i>Eriogonum fasciculatum</i>, <i>Adenostoma fasciculatum</i>, <i>Xylococcus bicolor</i> and <i>Malosma laurina</i>. The study site was burned in 1988 and has been recovering since then. El Mogor is a MexFlux site (MX-EMg; https://ameriflux.lbl.gov/sites/siteinfo/MX-EMg), equipped with an eddy covariance tower. The camera was static, ~ 3.5 m aboveground with a tilt pointing downward to the vegetation that is within the eddy covariance tower flux footprint, and facing North. The camera works with eight AA-batteries, and is equipped with 16 GB SD-memory card. Each camera took photographs in 30-minute intervals between 4:00 and 19:30 PST in a daily manner, but Ig was estimated using only photographs taken between 11:00 and 15:00 PST. Resolution of the photographs is 2304x1728 pixels. The image analysis included the definition of the region of the interest (ROI). From each pixel within the ROIs, the digital signature of the intensity of red, green, and blue were extracted and averaged on a per-day basis. Each digital signature has values of 0 to 255. To analyze the photographs, we used the Matlab-based PhenoCam GUI software, available from the PhenoCam Network (https://phenocam.sr.unh.edu/webcam/tools/). The greenness index is estimated as [Ig = Green/(Red + Green + Blue)].
DOI: 10.6084/m9.figshare.13280093.v1
title: Phenocam data from El Mogor, Baja California, Mexico, 2017

description: This dataset provides half-hourly observations of greenness index (I<sub>g</sub>), from one wildlife camera (M-550, Moultrie) for the year of 2017, at the study site of El Mogor, Baja California, Mexico (32.03017 N, 116.604219, 406 m asl). Vegetation at El Mogor is a mixture of chaparral and less-sclerophyllous species, including <i>Ornithostaphylos oppositifolia</i>, <i>Eriogonum fasciculatum</i>, <i>Adenostoma fasciculatum</i>, <i>Xylococcus bicolor</i> and <i>Malosma laurina</i>. The study site was burned in 1988 and has been recovering since then. El Mogor is a MexFlux site (MX-EMg; https://ameriflux.lbl.gov/sites/siteinfo/MX-EMg), equipped with an eddy covariance tower. The camera was static, ~ 3.5 m aboveground with a tilt pointing downward to the vegetation that is within the eddy covariance tower flux footprint, and facing North. The camera works with eight AA-batteries, and is equipped with 16 GB SD-memory card. Each camera took photographs in 30-minute intervals between 4:00 and 19:30 PST in a daily manner, but Ig was estimated using only photographs taken between 11:00 and 15:00 PST. Resolution of the photographs is 2304x1728 pixels. The image analysis included the definition of the region of the interest (ROI). From each pixel within the ROIs, the digital signature of the intensity of red, green, and blue were extracted and averaged on a per-day basis. Each digital signature has values of 0 to 255. To analyze the photographs, we used the Matlab-based PhenoCam GUI software, available from the PhenoCam Network (https://phenocam.sr.unh.edu/webcam/tools/). The greenness index is estimated as [Ig = Green/(Red + Green + Blue)].
DOI: 10.6084/m9.figshare.11931222.v2
title: Sample_Weight_Model for PredNet (Front. Psychol., 15 March 2018)

description: Sample weight model used in Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction (Front. Psychol., 15 March 2018; https://doi.org/10.3389/fpsyg.2018.00345).<br>This model was generated with predictive coding deep neural network using 500K FPSI video images.
DOI: 10.6084/m9.figshare.13204343
title: Data containing seed germination, seedling emergence, and seedling survival of 15 fig species (six hemiepiphytic species and nine non-hemiepiphytic species).xlsx

description: Data from Chen et al. 2020 contains seed germination, seedling emergence, and survival of 15 <i>Ficus </i>species (six hemiepiphytic species and nine non-hemiepiphytic species). Samples of mature drupelets were collected from south China from Yunnan and Guangxi provinces during 2016 to 2019 Experiments were conducted under the treatments of temperature (15/5°C, 25/15°C, 35/25°C; day/night diurnal variation) and water availability, as substrate moisture (0.00 MPa, -0.20 MPa, -0.35 MPa) and water vapor (99.5% relative humidity)
DOI: 10.6084/m9.figshare.13204343.v2
title: Data containing seed germination, seedling emergence, and seedling survival of 15 fig species (six hemiepiphytic species and nine non-hemiepiphytic species).xlsx

description: Data from Chen et al. 2020 contains seed germination, seedling emergence, and survival of 15 <i>Ficus </i>species (six hemiepiphytic species and nine non-hemiepiphytic species). Samples of mature drupelets were collected from south China from Yunnan and Guangxi provinces during 2016 to 2019 Experiments were conducted under the treatments of temperature (15/5°C, 25/15°C, 35/25°C; day/night diurnal variation) and water availability, as substrate moisture (0.00 MPa, -0.20 MPa, -0.35 MPa) and water vapor (99.5% relative humidity)
DOI: 10.6084/m9.figshare.13204343.v1
title: Chen et al. 2020_Tree Physiology.xlsx

description: Data from Chen et al. 2020 contains seed germination, seedling emergence, and survival of 15 <i>Ficus </i>species (six hemiepiphytic species and nine non-hemiepiphytic species). Samples of mature drupelets were collected from south China from Yunnan and Guangxi provinces during 2016 to 2019 Experiments were conducted under the treatments of temperature (15/5°C, 25/15°C, 35/25°C; day/night diurnal variation) and water availability, as substrate moisture (0.00 MPa, -0.20 MPa, -0.35 MPa) and water vapor (99.5% relative humidity)
DOI: 10.6084/m9.figshare.13280078
title: 3D skin tissue model Dataset

description: This is our generated 3D dataset which contains 3890 skin tissue vessels.
DOI: 10.6084/m9.figshare.13280078.v1
title: 3D skin tissue model Dataset

description: This is our generated 3D dataset which contains 3890 skin tissue vessels.
DOI: 10.21227/416j-3r62
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153 If you use this data, please cite the above paper.
DOI: 10.21227/hbyb-vy68
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153 If you use this data, please cite the above paper.
DOI: 10.21227/p93w-v959
title: Dataset: The perils and pitfalls of block design for EEG classification experiments

description: Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence"The perils and pitfalls of block design for EEG classification experiments"DOI: 10.1109/TPAMI.2020.2973153 If you use this ocde or data, please cite the above paper.