cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/6de-j7s-2f4
title: Himantoglossum robertianum (Loisel.) P.Delforge

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/8n7-t76-wv8
title: Fibigia clypeata (L.) Medik.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/dlg-e2s-2xn
title: Echium plantagineum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/rzk-rsd-25c
title: Echium angustifolium Thunb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7fq-m16-eyz
title: Polygonum equisetiforme Sm.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/k6p-yb0-k37
title: Himantoglossum robertianum (Loisel.) P.Delforge

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/5gb-gby-dvc
title: Fumaria vaillantii Loisel.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/yyh-byl-ayw
title: Festuca guestfalica Boenn. ex Rchb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/gtj-cfx-jgf
title: Alnus cordata (Loisel.) Duby

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wdl-drr-7ap
title: Ruscus aculeatus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/crv-a50-vc4
title: Lappula barbata (M. Bieb.) Gürke

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/p6a-ash-ng6
title: Lappula squarrosa (Retz.) Dumort.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/txvkt6f397
title: The Caucasus-Economic and Social Analysis Journal of Southern Caucasus

description: Agricultural, environmental & natural sciences
Social, pedagogy sciences & humanities
Medicine and biology sciences
Regional development and infrastructure
Economic, management & marketing sciences 
Legal, legislation and political sciences
Art and Film Studies
Cinematography
History of Science and Technics
Information, Computing and Automation
Innovative Technologies
DOI: 10.3535/ka1-7rc-nd3
title: Ruscus aculeatus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3k7-hey-3qd
title: Campanula persicifolia L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3w5-yjx-9kv
title: Lomelosia micrantha (Desf.) Greuter & Burdet

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.jmptpk
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oreochromis mossambicus",
 "Country is Grenada",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061731-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/txvkt6f397.1
title: The Caucasus-Economic and Social Analysis Journal of Southern Caucasus

description: Agricultural, environmental & natural sciences
Social, pedagogy sciences & humanities
Medicine and biology sciences
Regional development and infrastructure
Economic, management & marketing sciences 
Legal, legislation and political sciences
Art and Film Studies
Cinematography
History of Science and Technics
Information, Computing and Automation
Innovative Technologies
DOI: 10.3535/7e5-d7f-s1a
title: Epipactis helleborine (L.) Crantz

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lab-bwg-cxl
title: Linaria fastigiata Chav.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ahq-ecz-q58
title: Epipactis helleborine (L.) Crantz

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/p3y-jhf-tw5
title: Arum maculatum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/fff-l50-fmk
title: Elatine ambigua Wight

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/v7q-99y-jh4
title: Arum maculatum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/cl7-gxq-st2
title: Schweinfurthia papilionacea (L.) Boiss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.6084/m9.figshare.29274718
title: Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels

description: Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 – September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29274718.v1
title: Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels

description: Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 – September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.3535/31l-z5j-vt1
title: Astragalus karamasicus Boiss. & Balansa

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/6ck-52c-gwq
title: Astragalus karamasicus Boiss. & Balansa

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/m2w-bqy-jc2
title: Dittrichia graveolens (L.) Greuter

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wya-cnb-6af
title: Convolvulus lineatus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/95j-46t-ntv
title: Astragalus mesogitanus Boiss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/st2-lww-z74
title: Astragalus karamasicus Boiss. & Balansa

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2am-jtf-q82
title: Cerinthe minor L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2z1-1en-57m
title: Festuca ovina L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/9ff-ydr-c2h
title: Veronica serpyllifolia L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/e0z-17n-q0y
title: Anacamptis coriophora (L.) R.M.Bateman, Pridgeon & M.W.Chase

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/8jpxn4fr3x
title: NeuroOM: EEG Dataset Capturing Brain Responses to Musical and Non-Musical OM ("ॐ" ) Sound

description: Electroencephalography (EEG) is a popular non-invasive technology for recording electrical activity in
the brain, which can provide useful insights on cognitive and emotional responses to auditory stimuli.
In this work, we offer a unique EEG dataset gathered during OM ("ॐ" ) sound listening sessions, which is
intended to evaluate the brain processes related with musical and non-musical auditory experiences. The
data was collected using the Emotiv Insight headset, a five-channel EEG equipment that collects signals
at the AF3, AF4, T7, T8, and Pz electrode sites. A total of 132 EEG recordings are provided in European

Data Format (.edf) and classified into three levels of data processing: raw, standardized, and pre-
processed. Each stage contains data from two auditory conditions—musical OM ("ॐ" ) and non-musical OM ("ॐ" )—

which allow for an organized and comparative investigation of brain responses.
The raw data is divided into 44 files, 22 labeled as rm_.edf, representing raw EEG signals recorded
during musical OM ("ॐ" ) sound exposure, and 22 labeled as rn_.edf, indicating raw EEG signals recorded
during non-musical OM ("ॐ" ) sound exposure. These files preserve the original signal properties, including
noise and artifacts, and are designed for researchers working on custom preprocessing, signal quality
assessment, and artifact removal algorithms. The second stage includes standardized data, which consists
of 44 files designated as m_.edf and n_.edf, representing musical and non-musical OM ("ॐ" ) situations,
respectively. These files have been normalized and formatted to maintain uniformity across preparation
techniques and enable reproducible analysis. Finally, the dataset contains 44 pre-processed files labelled
pm_.edf and pn_.edf, which represent musical and non-musical OM ("ॐ" ) EEG data that has been filtered,
artifact-reduced, and segmented. These files are designed for direct input into machine learning models,
enabling tasks like classification, clustering, and feature extraction.
This dataset offers a unique chance to investigate the cognitive and affective impacts of OM ("ॐ" ) sound
listening, distinguishing between musical and non-musical stimuli. The data's multi-stage format allows
researchers to interact with EEG signals at various stages of processing, ranging from raw signal analysis
to model-ready inputs. The use of a commercially available EEG device provides accessibility and
reproducibility, making this dataset an important resource for research into auditory neuroscience,
emotional computing, and brain-computer interface development.
DOI: 10.3535/1b0-2g2-564
title: Adiantum capillus-veneris L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/bnq-vr2-jjc
title: Biscutella dufourii G. Mateo & M. B. Crespo

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/bwn-63k-rdk
title: Sparganium emersum Rehmann

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wan-nmx-qb5
title: Eleocharis ovata (Roth) Roem. & Schult.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/8jpxn4fr3x.2
title: NeuroOM: EEG Dataset Capturing Brain Responses to Musical and Non-Musical OM ("ॐ" ) Sound

description: Electroencephalography (EEG) is a popular non-invasive technology for recording electrical activity in
the brain, which can provide useful insights on cognitive and emotional responses to auditory stimuli.
In this work, we offer a unique EEG dataset gathered during OM ("ॐ" ) sound listening sessions, which is
intended to evaluate the brain processes related with musical and non-musical auditory experiences. The
data was collected using the Emotiv Insight headset, a five-channel EEG equipment that collects signals
at the AF3, AF4, T7, T8, and Pz electrode sites. A total of 132 EEG recordings are provided in European

Data Format (.edf) and classified into three levels of data processing: raw, standardized, and pre-
processed. Each stage contains data from two auditory conditions—musical OM ("ॐ" ) and non-musical OM ("ॐ" )—

which allow for an organized and comparative investigation of brain responses.
The raw data is divided into 44 files, 22 labeled as rm_.edf, representing raw EEG signals recorded
during musical OM ("ॐ" ) sound exposure, and 22 labeled as rn_.edf, indicating raw EEG signals recorded
during non-musical OM ("ॐ" ) sound exposure. These files preserve the original signal properties, including
noise and artifacts, and are designed for researchers working on custom preprocessing, signal quality
assessment, and artifact removal algorithms. The second stage includes standardized data, which consists
of 44 files designated as m_.edf and n_.edf, representing musical and non-musical OM ("ॐ" ) situations,
respectively. These files have been normalized and formatted to maintain uniformity across preparation
techniques and enable reproducible analysis. Finally, the dataset contains 44 pre-processed files labelled
pm_.edf and pn_.edf, which represent musical and non-musical OM ("ॐ" ) EEG data that has been filtered,
artifact-reduced, and segmented. These files are designed for direct input into machine learning models,
enabling tasks like classification, clustering, and feature extraction.
This dataset offers a unique chance to investigate the cognitive and affective impacts of OM ("ॐ" ) sound
listening, distinguishing between musical and non-musical stimuli. The data's multi-stage format allows
researchers to interact with EEG signals at various stages of processing, ranging from raw signal analysis
to model-ready inputs. The use of a commercially available EEG device provides accessibility and
reproducibility, making this dataset an important resource for research into auditory neuroscience,
emotional computing, and brain-computer interface development.
DOI: 10.3535/wxs-3gt-6nr
title: Isoetes olympica A. Br.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/6tn-em7-1gc
title: Biscutella laevigata L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/zt5-7kc-xlr
title: Asteraceae Dumort.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/9bg-5kf-zsk
title: Senecio L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/mwc-xvt-t26
title: Sparganium emersum Rehmann

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lyl-gbc-jkf
title: Eleocharis ovata (Roth) Roem. & Schult.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/x6q-zhw-9rq
title: Linum mucronatum subsp. armenum (Bordz.) P. H. Davis

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/n67gft8fvm
title: The Tails of Gravity

description: These files replicate all the results in the paper "The Tails of Gravity: Using Expectiles to Quantify the Trade-Margins Effects of Economic Integration Agreements" by Jeffrey H. Bergstrand, Matthew W. Clance, and J.M.C. Santos Silva.
DOI: 10.3535/080-p65-lsh
title: Ranunculus hederaceus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/g1w-e2v-ylf
title: Gymnadenia conopsea (L.) R.Br.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.a6cpbc
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oreochromis mossambicus",
 "Country is Haiti",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061732-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/5hz-knn-al1
title: Arabis auriculata Lam.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/n67gft8fvm.1
title: The Tails of Gravity

description: These files replicate all the results in the paper "The Tails of Gravity: Using Expectiles to Quantify the Trade-Margins Effects of Economic Integration Agreements" by Jeffrey H. Bergstrand, Matthew W. Clance, and J.M.C. Santos Silva.
DOI: 10.3535/1ah-yre-sjd
title: Astragalus cataonicus Bunge

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/vpz-c5k-xhs
title: Elatine triandra Schkuhr

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/66a-dj2-ln0
title: Ornithogalum comosum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/mlb-2cx-8t6
title: Cyperus esculentus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/8jmt7bjn8w
title: FSO Communication Parameters in Multi-User Systems under Variable Fog Conditions

description: This dataset comprises 3,000 data points collected from an advanced Free Space Optical (FSO) communication system designed for both SISO and MIMO configurations. The dataset captures key communication parameters, including range, Received Optical Power (ROP), Quality Factor (Q-factor), and Bit Error Rate (BER), under diverse fog conditions—Light Fog (LF), Medium Fog (MF), and Heavy Fog (HF). Each configuration (LF MIMO, HF MIMO, MF MIMO, LF SISO, HF SISO, MF SISO) is represented by 500 data points, offering a balanced distribution across the various scenarios.

The dataset is structured to support research on predictive modeling, handover optimization, and performance analysis in FSO networks. It has been preprocessed using a Min-Max normalization technique to ensure uniform scaling, making it ideal for machine learning applications such as regression and classification tasks. Detailed statistical summaries (mean, standard deviation, quartile ranges) and data distribution plots accompany the dataset, facilitating immediate use and benchmarking in studies of wireless communication performance under adverse atmospheric conditions.

Researchers can utilize this dataset to explore correlations between environmental factors and communication performance, develop adaptive handover strategies, and enhance overall FSO network reliability and efficiency. For further details and access, please contact the corresponding author.
DOI: 10.3535/b5m-377-s28
title: Papaver rhoeas L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/qkb-941-lvz
title: Gymnadenia conopsea (L.) R.Br.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/xnw-ayz-bc0
title: Astragalus angustifolius Lam.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/8jmt7bjn8w.2
title: FSO Communication Parameters in Multi-User Systems under Variable Fog Conditions

description: This dataset comprises 3,000 data points collected from an advanced Free Space Optical (FSO) communication system designed for both SISO and MIMO configurations. The dataset captures key communication parameters, including range, Received Optical Power (ROP), Quality Factor (Q-factor), and Bit Error Rate (BER), under diverse fog conditions—Light Fog (LF), Medium Fog (MF), and Heavy Fog (HF). Each configuration (LF MIMO, HF MIMO, MF MIMO, LF SISO, HF SISO, MF SISO) is represented by 500 data points, offering a balanced distribution across the various scenarios.

The dataset is structured to support research on predictive modeling, handover optimization, and performance analysis in FSO networks. It has been preprocessed using a Min-Max normalization technique to ensure uniform scaling, making it ideal for machine learning applications such as regression and classification tasks. Detailed statistical summaries (mean, standard deviation, quartile ranges) and data distribution plots accompany the dataset, facilitating immediate use and benchmarking in studies of wireless communication performance under adverse atmospheric conditions.

Researchers can utilize this dataset to explore correlations between environmental factors and communication performance, develop adaptive handover strategies, and enhance overall FSO network reliability and efficiency. For further details and access, please contact the corresponding author.
DOI: 10.3535/01a-zb7-g62
title: Astragalus mesogitanus Boiss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/xgz-4rs-zla
title: Acanthus spinosus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ram-7t9-k1w
title: Prunella laciniata (L.) L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/t3r-wea-53m
title: Stipa holosericea Trin.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/wrmr72cys8
title: EXPLORING SOCIAL MEDIA ADDICTION: THE CASE OF TIKTOK USE IN GEN Z IN JAKARTA

description: The data consists of the Indonesian population and a sample from the DKI Jakarta Province, with special consideration given to Generation Z users of the TikTok app.
DOI: 10.3535/e80-jls-t3g
title: Cyperus microiria Steud.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/tj3-jd6-t1f
title: Epipactis helleborine (L.) Crantz

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wy9-fay-625
title: Nerium oleander L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/wrmr72cys8.1
title: EXPLORING SOCIAL MEDIA ADDICTION: THE CASE OF TIKTOK USE IN GEN Z IN JAKARTA

description: The data consists of the Indonesian population and a sample from the DKI Jakarta Province, with special consideration given to Generation Z users of the TikTok app.
DOI: 10.3535/07q-7zk-37h
title: Ranunculus tripartitus DC.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/66j-mk1-wsc
title: Abies alba Mill.

description: Digital Specimen for the physical specimen hosted at University of Graz.