cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6587249
title: Hans Havemann: Italienische Reise (1907) – Manuskript und Transkript

description: Wir präsentieren ein Digitalisat eines Manuskripts von Hans Havemann (1887–1985), in welchem er seine Reise nach Italien im Jahr 1907 beschreibt. Er wanderte als Student der Philosophie zusammen mit dem Maler und Erfinder Ernst Neumann-Neander (1871–1954) von Genua über Pisa und Siena nach Perugia und verfasste darüber diesen literarischen Text, den er aber nicht veröffentlichen konnte. Als Urenkelin und Enkel wanderten wir die Route der Beiden nach und transkribierten dafür das Manuskript. Das Digitalisat erscheint in drei Stufen: Bilder (PDF, DIN-A4) des Deckblatts und der 198 Manuskriptseiten komprimiert in der Datei scan.zip, zeilentreues Transkript als ASCII-Datei (Kodierung: ISO-Latin 1), die als LaTeX-Quelldatei dient: Transkript-Havemann-Italien.tex, PDF-Datei der Druckvorlage für eine Buchveröffentlichung (DIN-A5): Havemann-Italienische-Reise-Doemitz-Druckvorlage.pdf. 2022 haben wir den Text als Buch herausgegeben (s. Referenz). Petra und Reinhold Kraft zeigten in ihrer Galerie in Dömitz Bilder, die E. Neumann-Neander während der Reise 1907 anfertigte, und Textteile aus der Reisebeschreibung von H. Havemann (vgl. Datei Handzettel-Italienische-Reisen-v5.pdf). Diese Ausstellung wurde im Winter 2023 auch im Museum in Grabow, der Geburtsstadt von Hans Havemann, gezeigt (https://www.grabow.de/events/italienische-reisen).
description: In den Nachbemerkungen des Buches sind die Regeln nachzulesen, nach denen wir den Text transkribiert haben. Außerdem editorische und historische Anmerkungen sowie persönliche Erinnerungen. Im Anhang befinden sich drei Transkripte: 1. eines Briefs von Hans Havemann an Martin Buber (vom 19. 10. 1911), 2. einer Notiz von Ernst Neumann-Neander in Siena (18. April 1907), und 3. eines Tagebucheintrags von Ernst Neumann-Neander (13. März 1950). Alle drei Dokumente und zwei farbige Skizzen, die Neumann 1907 in Genua bzw. Siena anfertigte, sind als Bilddateien deponiert (Havemann-Buber-Okt.1911.pdf, Neumann-Notiz-Siena.tiff, Neumann1950.pdf, Neumann1907Genua.tiff, Neumann1907Siena.tiff). Deponiert sind außerdem die LaTeX-Quelldatei für die Druckvorlage (Havemann-Italienische-Reise-Doemitz-Druckvorl.tex), die von Paul Berthold erstellte Datei für das Transkriptions-usepackage (italien190x.sty) sowie die verwendete bibtex-Datei (Havemann.bib). Außerdem das im Buch abgedruckte Foto von Hans Havemann (Hans-Havemann-1905-1906.jpg).
description: {"references": ["Havemann, Hans (2022). Italienische Reise. Herausgegeben von Marie Sander und Frank Havemann. Verlag: Atelier Kraft, D\u00f6mitz"]}
DOI: 10.5281/zenodo.6587250
title: Hans Havemann: Italienische Reise (1907) – Manuskript und Transkript

description: Wir präsentieren ein Digitalisat eines Manuskripts von Hans Havemann (1887–1985), in welchem er seine Reise nach Italien im Jahr 1907 beschreibt. Er wanderte als Student der Philosophie zusammen mit dem Maler und Erfinder Ernst Neumann-Neander (1871–1954) von Genua über Pisa und Siena nach Perugia und verfasste darüber diesen literarischen Text, den er aber nicht veröffentlichen konnte. Als Urenkelin und Enkel wanderten wir die Route der Beiden nach und transkribierten dafür das Manuskript. Das Digitalisat erscheint in drei Stufen: Bilder (PDF, DIN-A4) des Deckblatts und der 198 Manuskriptseiten komprimiert in der Datei scan.zip, zeilentreues Transkript als ASCII-Datei (Kodierung: ISO-Latin 1), die als LaTeX-Quelldatei dient: Transkript-Havemann-Italien.tex, PDF-Datei der Druckvorlage für eine Buchveröffentlichung (DIN-A5): Havemann-Italienische-Reise-Doemitz-Druckvorlage.pdf. 2022 haben wir den Text als Buch herausgegeben (s. Referenz). Petra und Reinhold Kraft zeigten in ihrer Galerie in Dömitz Bilder, die E. Neumann-Neander während der Reise 1907 anfertigte, und Textteile aus der Reisebeschreibung von H. Havemann (vgl. Datei Handzettel-Italienische-Reisen-v5.pdf). Diese Ausstellung wurde im Winter 2023 auch im Museum in Grabow, der Geburtsstadt von Hans Havemann, gezeigt (https://www.grabow.de/events/italienische-reisen).
description: In den Nachbemerkungen des Buches sind die Regeln nachzulesen, nach denen wir den Text transkribiert haben. Außerdem editorische und historische Anmerkungen sowie persönliche Erinnerungen. Im Anhang befinden sich drei Transkripte: 1. eines Briefs von Hans Havemann an Martin Buber (vom 19. 10. 1911), 2. einer Notiz von Ernst Neumann-Neander in Siena (18. April 1907), und 3. eines Tagebucheintrags von Ernst Neumann-Neander (13. März 1950). Alle drei Dokumente und zwei farbige Skizzen, die Neumann 1907 in Genua bzw. Siena anfertigte, sind als Bilddateien deponiert (Havemann-Buber-Okt.1911.pdf, Neumann-Notiz-Siena.tiff, Neumann1950.pdf, Neumann1907Genua.tiff, Neumann1907Siena.tiff). Deponiert sind außerdem die LaTeX-Quelldatei für die Druckvorlage (Havemann-Italienische-Reise-Doemitz-Druckvorl.tex), die von Paul Berthold erstellte Datei für das Transkriptions-usepackage (italien190x.sty) sowie die verwendete bibtex-Datei (Havemann.bib). Außerdem das im Buch abgedruckte Foto von Hans Havemann (Hans-Havemann-1905-1906.jpg).
description: {"references": ["Havemann, Hans (2022). Italienische Reise. Herausgegeben von Marie Sander und Frank Havemann. Verlag: Atelier Kraft, D\u00f6mitz"]}
DOI: 10.57841/casdc.0000504
title: Y3Fe5O12/CoO/Y3Fe5O12绝缘异质结构中的磁振子结效应
DOI: 10.57841/casdc.0000505
title: 原子尺度观察SrFeO2.5薄膜中从钙铁石到无限层的结构转变
DOI: 10.57841/casdc.0000506
title: 斯格明子泡的手性翻转
DOI: 10.5281/zenodo.5818361
title: Predicting the execution time of COSMO weather forecast models

description: This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna. Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models.<br> A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users. This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.
DOI: 10.5281/zenodo.5818362
title: Predicting the execution time of COSMO weather forecast models

description: This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna. Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models.<br> A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users. This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.
DOI: 10.57841/casdc.0000507
title: 基于砷化镓的p-i-n结构的薄有源区的增强吸收过程
DOI: 10.57841/casdc.0000508
title: 通过磁子传递扭矩切换磁绝缘体的垂直磁化作用
DOI: 10.17632/fgjhk5rwmr.1
title: FACTORS TRIGGERING NON-PERFORMING ASSETS IN APPRAISAL STAGE OF LOANS IN INDIAN COMMERCIAL BANKS- AN EMPIRICAL STUDY

description: Non-Performing Asset (NPAs) is generated in three stages of loan proposal i.e., appraisal stage, sanction and disbursal stage and post disbursal stage. The first stage is the appraisal stage of a loan where precondition of a loan proposal is analysed. The study aims to find out the causes of NPAs at appraisal stage through primary survey among the credit officers of the bank. Factor analysis is performed to point out the important factors that are responsible for generation of NPAs due to faulty appraisal of a loan. Three major factors i.e. lack of knowledge about exposure, organization
failure, and turnaround time (TAT) were obtained through analysis for which major exposure become NPAs. Finally ANOVA, Post-Hoc test between the groups is performed based on the demographic parameters i.e. type of  organization they are working, Name of the department the concerned officer is working, and scale of the officers.
DOI: 10.17632/ttrt2fj8xh.1
title: effect of psychosomatic factors on stomatgnathic system among health care students  

description: physical nd psychological factors collectively can be called as psychosomatic disorder which is a medical ailment or set of symptoms brought on by psychological reasons that also poses a threat to the person's well-being. This study aims to study to investigate the impact of physical and psychological factors on stomatognathic system among health care students. Research is conducted by forming google form questionnaire including questions related to physical and psychological factors, questions related to  facial pain and questions related to stomatognathic dysfunction like gingival recession, linea alba and tongue crenation. At the end of data collection and after analyzing it, it was found that psychological and physical factors does not have much significant effect on stomatognathic system, very less number of participants were having any kind of facial pain due to any psychsomatic factor, level of linea alba, tongue crenation and gingival recession was also very low .
DOI: 10.57841/casdc.0000509
title: CaRuO3/SrTiO3超晶格界面层中的对称性错配诱导的铁磁效应
DOI: 10.5281/zenodo.4537849
title: Examon data from Marconi HPC system (snapshot)

description: This data has been gathered and analyzed by Massimo Schembri during his internship at University of Bologna, with the supervision of Andrea Borghesi (assistant professor at the same university). The data was collected from a monitored supercomputer hosted at CINECA and called "Marconi"; the data was collected with a holistic data monitoring infrastructure called Examon, developed by researchers from the University of Bologna with the collaborations of CINECA system administrators. The data set is composed of two monitored periods January and May 2020; for these two periods, there is data relating to a subset of the nodes in Marconi supercomputer. The information monitored on Marconi's nodes is varied, ranging from the load of the different cores, to the temperature of the room where the nodes are located, the speed of the fans, details on memory accesses in writing / reading, etc. The sampling rate of the data at the source varies between 5 and 10 seconds. However, in your data set the data are aggregated in 5-minutes intervals; in particular, the mean value ("avg: &lt;metric_name&gt;") and variance ("var: &lt;metric_name&gt;") are computed over each 5-minute interval. In the CSVs, each row corresponds to a different timestamp (first column on the left), therefore separated by intervals of 5 minutes. For example, if you a timestamp equal to "2020-01-01 02:10:00" this indicates that the mean and variance values were calculated in the previous 5 minutes (2020-01-01 02:05:00 - 2020- 01-01 02:10:00). The remaining columns (apart from the last two) contain the aggregate metrics (mean and variance). The last column "Jobs" indicates the number of applications (called HPC jobs) finished on the node in the last half hour. The penultimate "Label" column indicates the presence or absence of a failure on the node (as registered using Nagios service).
description: This work was partially supported by EU H2020 IoTwins Innovation Action project (g.a. 857191). We also want to thank CINECA for granting us the access to their systems.
DOI: 10.5281/zenodo.4537850
title: Examon data from Marconi HPC system (snapshot)

description: This data has been gathered and analyzed by Massimo Schembri during his internship at University of Bologna, with the supervision of Andrea Borghesi (assistant professor at the same university). The data was collected from a monitored supercomputer hosted at CINECA and called "Marconi"; the data was collected with a holistic data monitoring infrastructure called Examon, developed by researchers from the University of Bologna with the collaborations of CINECA system administrators. The data set is composed of two monitored periods January and May 2020; for these two periods, there is data relating to a subset of the nodes in Marconi supercomputer. The information monitored on Marconi's nodes is varied, ranging from the load of the different cores, to the temperature of the room where the nodes are located, the speed of the fans, details on memory accesses in writing / reading, etc. The sampling rate of the data at the source varies between 5 and 10 seconds. However, in your data set the data are aggregated in 5-minutes intervals; in particular, the mean value ("avg: &lt;metric_name&gt;") and variance ("var: &lt;metric_name&gt;") are computed over each 5-minute interval. In the CSVs, each row corresponds to a different timestamp (first column on the left), therefore separated by intervals of 5 minutes. For example, if you a timestamp equal to "2020-01-01 02:10:00" this indicates that the mean and variance values were calculated in the previous 5 minutes (2020-01-01 02:05:00 - 2020- 01-01 02:10:00). The remaining columns (apart from the last two) contain the aggregate metrics (mean and variance). The last column "Jobs" indicates the number of applications (called HPC jobs) finished on the node in the last half hour. The penultimate "Label" column indicates the presence or absence of a failure on the node (as registered using Nagios service).
description: This work was partially supported by EU H2020 IoTwins Innovation Action project (g.a. 857191). We also want to thank CINECA for granting us the access to their systems.
DOI: 10.17632/mmt9gj4c4d.1
title: Monitored precipitation and discharge in Zhonghe Watershed

description: Monitored data sets in GRL: "Abrupt change in the sediment export in the postseismic watershed via the altered erosion state"
DOI: 10.17632/mmt9gj4c4d.2
title: Monitored precipitation and discharge in Zhonghe Watershed

description: Monitored data sets in GRL: "Abrupt change in the sediment export in the postseismic watershed via the altered erosion state"
DOI: 10.5281/zenodo.3251873
title: Data set for anomaly detection on a HPC system

description: This data set contains the data collected on the DAVIDE HPC system (CINECA & E4 & University of Bologna, Bologna, Italy) in the period March-May 2018. The data set has been used to train a autoencoder-based model to automatically detect anomalies in a semi-supervised fashion, on a real HPC system. This work is described in: 1) "Anomaly Detection using Autoencoders in High Performance Computing Systems", Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca Benini, IAAI19 (proceedings in process) -- https://arxiv.org/abs/1902.08447 2) "Online Anomaly Detection in HPC Systems", Andrea Borghesi, Antonio Libri, Luca Benini, Andrea Bartolini, AICAS19 (proceedings in process) -- https://arxiv.org/abs/1811.05269 See the git repository for usage examples & details --&gt; https://github.com/AndreaBorghesi/anomaly_detection_HPC
description: {"references": ["Borghesi et al. 2019 https://arxiv.org/abs/1811.05269", "Borghesi et al. 2019 https://arxiv.org/abs/1902.08447"]}
DOI: 10.5281/zenodo.3251872
title: Data set for anomaly detection on a HPC system

description: This data set contains the data collected on the DAVIDE HPC system (CINECA & E4 & University of Bologna, Bologna, Italy) in the period March-May 2018. The data set has been used to train a autoencoder-based model to automatically detect anomalies in a semi-supervised fashion, on a real HPC system. This work is described in: 1) "Anomaly Detection using Autoencoders in High Performance Computing Systems", Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca Benini, IAAI19 (proceedings in process) -- https://arxiv.org/abs/1902.08447 2) "Online Anomaly Detection in HPC Systems", Andrea Borghesi, Antonio Libri, Luca Benini, Andrea Bartolini, AICAS19 (proceedings in process) -- https://arxiv.org/abs/1811.05269 See the git repository for usage examples & details --&gt; https://github.com/AndreaBorghesi/anomaly_detection_HPC
description: {"references": ["Borghesi et al. 2019 https://arxiv.org/abs/1811.05269", "Borghesi et al. 2019 https://arxiv.org/abs/1902.08447"]}
DOI: 10.5066/p1gyskbs
title: Geochemical and spectroscopic data on core samples from Lake Tanganyika in Tanzania

description: This data release contains geochemical and spectroscopic laboratory results associated with the journal article "Middle-Late Holocene paleolimnological changes in central Lake Tanganyika: Integrated evidence from the Kavala Island Ridge (Tanzania)". Data include bulk organic geochemistry, organic stable carbon isotope, and infrared spectroscopic analyses. Location for LT-TANG17-9B-1U-1: latitude: -5.952900, longitude: 29.639700. Coring site is located ~ 14 km offshore in ~ 420 m of water.
DOI: 10.6084/m9.figshare.9163265.v1
title: Additional file 2: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Figure S1. Grain Cd concentration between bread wheat and durum wheat. Figure S2. A Box plot of miRNA. B Correlation analysis of expression levels of miRNAs between samples and samples. Figure S3. A Box plot of mRNA. B Correlation analysis of expression levels of mRNAs between samples and samples. Figure S4. A The volcano plot illustrates the distribution of mRNA expression fold changes vs P values between L17Cd and L17CK. B The volcano plot illustrates the distribution of the data in mRNA profiles between H17Cd and HL17CK. Red, green and black points in the volcano plot represents significantly upregulated mRNAs, significantly downregulated mRNAs and not differential expressed mRNAs, respectively. Figure S5. GO enrichment analysis of differentially-expressed miRNA-targeted genes. The top 10 GOs enriched in targeted genes are given in response to miRNAs: (A) down-regulated and (B) up-regulated between L17Cd and L17CK; (C) down-regulated (D) up-regulated between H17Cd and H17CK. The y-axis represent the number of genes enriched. Figure S6. Venn diagrams of mRNA Gene Ontology (GO) enrichment results. Overlap of GO results of differentially expressed microRNAs (DEMs) between L17Cd and L17CK, H17Cd and H17CK. B Venn diagram for GO of DEGs between L17Cd and L17CK, H17Cd and H17CK. Figure S7. GO analysis for differentially-expressed mRNAs. The top 10 GOs enriched in differentially expressed mRNAs are given for the contrasts: (A) up-regulated and (B) down-regulated between L17Cd and L17CK; (C) up-regulated and (D) down-regulated between H17Cd and H17CK. The y-axis represent the gene number enriched in GOs. Figure S8. Ten wheat HMAs motifs were identified by MEME tools and indicated by different color. Motif location and combined p-value were represented. (ZIP 1392 kb)
DOI: 10.6084/m9.figshare.9163271
title: Additional file 3: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Functional annotation of transport genes. (XLSX 27 kb)
DOI: 10.6084/m9.figshare.20173277.v1
title: Dataset 1.pdf

description: The 197 eco-friendly glass models and their order in the 2021 CDGM glass library that we use in the paper.
DOI: 10.6084/m9.figshare.9163277
title: Additional file 4: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Primers for genes. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.9163271.v1
title: Additional file 3: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Functional annotation of transport genes. (XLSX 27 kb)
DOI: 10.6084/m9.figshare.9163262.v1
title: Additional file 1: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Grain Cd concentration among wheat germplasms. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.9163277.v1
title: Additional file 4: of The genome-wide impact of cadmium on microRNA and mRNA expression in contrasting Cd responsive wheat genotypes

description: Primers for genes. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.13357529
title: Additional file 2 of Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer’s disease and implicate its dynamic role in disease progression

description: Additional file 2. Table S2: P values and significant slopes for the regression lines modeled between alternate allelic doses of rs708727/rs960603 and β values for the 10 CpG probes at PM20D1 promoter region in the different diagnosis groups.
DOI: 10.6084/m9.figshare.13357529.v1
title: Additional file 2 of Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer’s disease and implicate its dynamic role in disease progression

description: Additional file 2. Table S2: P values and significant slopes for the regression lines modeled between alternate allelic doses of rs708727/rs960603 and β values for the 10 CpG probes at PM20D1 promoter region in the different diagnosis groups.
DOI: 10.6084/m9.figshare.13357532
title: Additional file 3 of Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer’s disease and implicate its dynamic role in disease progression

description: Additional file 3. Table S3: P values and significant slopes for the regression lines modeled by linear mixed effect modeling between age or p-tau181 and β values for the 10 CpG probes at PM20D1 promoter region in the different diagnosis groups.
DOI: 10.6084/m9.figshare.13357538
title: Additional file 5 of Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer’s disease and implicate its dynamic role in disease progression

description: Additional file 5. Table S5: P values for the regression lines between Braak score, amyloid or tangles and β values for the 8 CpG probes at PM20D1 promoter region in the different diagnosis groups from the ROSMAP cohort brain samples.