cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8214051
title: [IO Islamic 2484] رقعات جامى

description: <strong>Ruḳa’ât-i-Jâmî.</strong> <strong>This manuscript is now IO Islamic 1389 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 2484 here with notations and hyperlinks].</strong> 1389 A still more defective copy of the same. This copy is definitely both at the beginning and end; it opens abruptly, just five words before the beginning of the preceding copy: ...جمع كرده شد و ترتيب داده آمد الخ , and breaks off on fol. 82<sup>b</sup>. The exact words, with which this copy ends, cannot be traced in the two preceding copies, as the arrangement of letters differs in some points, but fol. 82<sup>a</sup>, l. 8, corresponds to fol. 78<sup>a</sup>, l. 3 in the preceding copy. Many pages are slightly injured. The proper order of the leaves is: ff. 1 -15, 17-20, 16, 21-82; there are besides lacunas after ff. 6 and 15. Bibliotheca Leydeniana. No. 2484, ff. 1-82, ll. 13; Nasta’lîḳ; size, 8<sup>1/2</sup> in. by 4<sup>5/8</sup> in.
DOI: 10.5281/zenodo.8214050
title: [IO Islamic 2484] رقعات جامى

description: <strong>Ruḳa’ât-i-Jâmî.</strong> <strong>This manuscript is now IO Islamic 1389 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 2484 here with notations and hyperlinks].</strong> 1389 A still more defective copy of the same. This copy is definitely both at the beginning and end; it opens abruptly, just five words before the beginning of the preceding copy: ...جمع كرده شد و ترتيب داده آمد الخ , and breaks off on fol. 82<sup>b</sup>. The exact words, with which this copy ends, cannot be traced in the two preceding copies, as the arrangement of letters differs in some points, but fol. 82<sup>a</sup>, l. 8, corresponds to fol. 78<sup>a</sup>, l. 3 in the preceding copy. Many pages are slightly injured. The proper order of the leaves is: ff. 1 -15, 17-20, 16, 21-82; there are besides lacunas after ff. 6 and 15. Bibliotheca Leydeniana. No. 2484, ff. 1-82, ll. 13; Nasta’lîḳ; size, 8<sup>1/2</sup> in. by 4<sup>5/8</sup> in.
DOI: 10.5281/zenodo.8214059
title: [IO Islamic 273] باغ ارم

description: <strong>Jâmî's-Bâgh-i-Iram.</strong> <strong>This manuscript is now IO Islamic 1390 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 273 here with notations and hyperlinks].</strong> <em>Poets who died between</em> A.H. 900 <em>and</em> 1000. 1390 Bâgh-i-Iram (باغ ارم). The garden of Iram, or the story of Bahrâm and Bihrûz, an epic poem by Maulânâ Kamâl-aldîn Bannâ’î of Harât, who was killed in the massacre of Shâh Isma’îl at Samarḳand, A.H. 918 (A.D. 1512, 1513); comp. on the poet’s life and works Bodleian Cat., No. 987; A. Sprenger, Catal., p. 372; A. F. Mehren, p. 41; Notices et Extraits iv. p. 289; see also Haft Iḳlîm, No. 635 (col. 424 in this Cat.); Âtashkada, No. 291 (ib. col. 273), etc. The poem is usually styled بهرام و بهروز (see also Rieu i. p. 351<sup>b</sup>); but the genuine title appears here, on fol. 33<sup>a</sup>, l. 6: نام او شد نهال باغ ارم – تا كه روشن كند چراغ ارم (the word نهال being an apparent misspelling for نهاده, as the Bodleian copy has); in his lyrical poems Bannâ’î used Ḥâlî as takhalluṣ (see Âtashkada and Sprenger, loc. cit.). Beginning of the poem, on fol. 1<sup>b</sup>: اى وجود تواصل كلّ وجود – هستى و بودۀ و خواهى بود (an initial bait which has been closely imitated by Hilâlî in his شاه و گدا, see further below, Nos. 1426 – 1429); on fol. 1<sup>a</sup>, the author is called by mistake Thanâ’î instead of Bannâ’î. The right order of ff. 262-265 is: 262, 264, 263, 265. No. 273, ff. 269, 2 coll., each ll. 15; Nasta’lîḳ; collated throughout; size, 8<sup>1/8</sup> in. by 4<sup>3/8</sup> in. <br>
DOI: 10.5281/zenodo.8214058
title: [IO Islamic 273] باغ ارم

description: <strong>Jâmî's-Bâgh-i-Iram.</strong> <strong>This manuscript is now IO Islamic 1390 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 273 here with notations and hyperlinks].</strong> <em>Poets who died between</em> A.H. 900 <em>and</em> 1000. 1390 Bâgh-i-Iram (باغ ارم). The garden of Iram, or the story of Bahrâm and Bihrûz, an epic poem by Maulânâ Kamâl-aldîn Bannâ’î of Harât, who was killed in the massacre of Shâh Isma’îl at Samarḳand, A.H. 918 (A.D. 1512, 1513); comp. on the poet’s life and works Bodleian Cat., No. 987; A. Sprenger, Catal., p. 372; A. F. Mehren, p. 41; Notices et Extraits iv. p. 289; see also Haft Iḳlîm, No. 635 (col. 424 in this Cat.); Âtashkada, No. 291 (ib. col. 273), etc. The poem is usually styled بهرام و بهروز (see also Rieu i. p. 351<sup>b</sup>); but the genuine title appears here, on fol. 33<sup>a</sup>, l. 6: نام او شد نهال باغ ارم – تا كه روشن كند چراغ ارم (the word نهال being an apparent misspelling for نهاده, as the Bodleian copy has); in his lyrical poems Bannâ’î used Ḥâlî as takhalluṣ (see Âtashkada and Sprenger, loc. cit.). Beginning of the poem, on fol. 1<sup>b</sup>: اى وجود تواصل كلّ وجود – هستى و بودۀ و خواهى بود (an initial bait which has been closely imitated by Hilâlî in his شاه و گدا, see further below, Nos. 1426 – 1429); on fol. 1<sup>a</sup>, the author is called by mistake Thanâ’î instead of Bannâ’î. The right order of ff. 262-265 is: 262, 264, 263, 265. No. 273, ff. 269, 2 coll., each ll. 15; Nasta’lîḳ; collated throughout; size, 8<sup>1/8</sup> in. by 4<sup>3/8</sup> in. <br>
DOI: 10.6084/m9.figshare.23812830.v44
title: Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiratio

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset:14 CMIP6 GCMs (1850-2014)Future dataset: 14CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.6084/m9.figshare.21840392
title: Predicting volumetric error compensation for five-axis machine tool using machine learning

description: This work proposes a rapid and robust machine-learning model to predict the volumetric error of a five-axis machine tool. For this purpose, several machine learning models – which are MultiOutput regression, Chained MultiOutput regression, Linear regression, SVM regression, XGB regression and ANN regression – have been selected, and their performances were compared to other models in the literature and to one another to find the best model for the problem at hand. The robustness of each model is investigated using three statistical metrics: namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R<sup>2</sup>). The results show that the models proposed in this paper are more effective than those in the literature, using the same data. Amongst the proposed models, the SVR Regression model has proven to be the best, considering all statistical metrics. Compared to the polynomial method in the literature with varying order levels, the method proposed here improves accuracy and predictive performance by 27%, with an RMSE of 0.03246 mm for the SVR model. Finally, based on the best model, an explicit equation has been deduced for practical applications. That prediction equation has been implemented in Excel and included in this paper.
DOI: 10.6084/m9.figshare.21840392.v1
title: Predicting volumetric error compensation for five-axis machine tool using machine learning

description: This work proposes a rapid and robust machine-learning model to predict the volumetric error of a five-axis machine tool. For this purpose, several machine learning models – which are MultiOutput regression, Chained MultiOutput regression, Linear regression, SVM regression, XGB regression and ANN regression – have been selected, and their performances were compared to other models in the literature and to one another to find the best model for the problem at hand. The robustness of each model is investigated using three statistical metrics: namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R<sup>2</sup>). The results show that the models proposed in this paper are more effective than those in the literature, using the same data. Amongst the proposed models, the SVR Regression model has proven to be the best, considering all statistical metrics. Compared to the polynomial method in the literature with varying order levels, the method proposed here improves accuracy and predictive performance by 27%, with an RMSE of 0.03246 mm for the SVR model. Finally, based on the best model, an explicit equation has been deduced for practical applications. That prediction equation has been implemented in Excel and included in this paper.
DOI: 10.6084/m9.figshare.21900870.v1
title: Shielding effect enables fast ion transfer through nanoporous membrane for highly energy-efficient electrodialysis

description: This document contains source dataset for key experimental results
DOI: 10.6084/m9.figshare.21900870
title: Shielding effect enables fast ion transfer through nanoporous membrane for highly energy-efficient electrodialysis

description: This document contains source dataset for key experimental results
DOI: 10.6084/m9.figshare.22308637.v1
title: Lowland river sinuosity on Earth and Mars set by pace of meandering and avulsion

description: Channel path shape files for sinuosity and migration rate
DOI: 10.6084/m9.figshare.22308637
title: Lowland river sinuosity on Earth and Mars set by pace of meandering and avulsion

description: Channel path shape files for sinuosity and migration rate
DOI: 10.6084/m9.figshare.22782977.v1
title: WNTinib is a multi-kinase inhibitor with specificity against β-catenin mutant hepatocellular carcinoma

description: Xlsx files
DOI: 10.6084/m9.figshare.22782977
title: WNTinib is a multi-kinase inhibitor with specificity against β-catenin mutant hepatocellular carcinoma

description: Xlsx files
DOI: 10.6084/m9.figshare.23812830.v45
title: Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiratio

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset:14 CMIP6 GCMs (1850-2014)Future dataset: 14CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.14457/nida.the.2003.393
title: The implementation of downsizing of the Royal Thai Army
DOI: 10.6084/m9.figshare.23583423
title: Article RAW data

description: <br><b>PI3K/Akt signaling pathway in retinal ganglion cells</b>
DOI: 10.6084/m9.figshare.23583423.v1
title: Article RAW data

description: <br><b>PI3K/Akt signaling pathway in retinal ganglion cells</b>
DOI: 10.6084/m9.figshare.23812830.v46
title: Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiratio

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset:14 CMIP6 GCMs (1850-2014)Future dataset: 14CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.34740/kaggle/dsv/6246312
title: Potato Pests dataset
DOI: 10.5281/zenodo.8142188
title: 4.5 years of peatland forest N2O flux data data measured using automatic chambers

description: The data contains daily mean N2O fluxes and supporting environmental data from June 2015 to September 2019. The measurement site is Lettosuo peatland forest (ICOS associate site, FI-Let) located in Tammela, Finland. The site is nutrient-rich and drained for forestry in 1969. Light selection harvest was done at the automatic chamber location in March 2016. Six automatic chambers operated year-round and each chamber measured N2O flux once in an hour. Environmental variables were measured close to the automatic chambers or in the nearest automatic weather station. For more information about the site and automatic chamber system, see Koskinen et al., (2014) and Korkiakoski et al. (2017, 2020).
description: {"references": ["Korkiakoski, M., Tuovinen, J. P., Aurela, M., Koskinen, M., Minkkinen, K., Ojanen, P., Penttil\u00e4, T., Rainne, J., Laurila, T., and Lohila, A.: Methane exchange at the peatland forest floor \u2013 automatic chamber system exposes the dynamics of small fluxes, Biogeosciences, 14(7), 1947\u20131967, https://doi.org/10.5194/bg-14-1947-2017, 2017", "Korkiakoski, M., Ojanen, P., Penttil\u00e4, T., Minkkinen, K., Sarkkola, S., Rainne, J., Laurila, T., and Lohila, A.: Impact of partial harvest on CH4 and N2O balances of a drained boreal peatland forest, Agric. For. Meteorol., 295, 108168, https://doi.org/10.1016/j.agrformet.2020.108168, 2020", "Koskinen, M., Minkkinen, K., Ojanen, P., K\u00e4m\u00e4r\u00e4inen, M., Laurila, T., and Lohila, A.: Measurements of CO2 exchange with an automated chamber system throughout the year: challenges in measuring night-time respiration on porous peat soil, Biogeosciences, 11(2), 347\u2013363, https://doi.org/10.5194/bg-11-347-2014, 2014"]}
DOI: 10.5281/zenodo.8142187
title: 4.5 years of peatland forest N2O flux data data measured using automatic chambers

description: The data contains daily mean N2O fluxes and supporting environmental data from June 2015 to September 2019. The measurement site is Lettosuo peatland forest (ICOS associate site, FI-Let) located in Tammela, Finland. The site is nutrient-rich and drained for forestry in 1969. Light selection harvest was done at the automatic chamber location in March 2016. Six automatic chambers operated year-round and each chamber measured N2O flux once in an hour. Environmental variables were measured close to the automatic chambers or in the nearest automatic weather station. For more information about the site and automatic chamber system, see Koskinen et al., (2014) and Korkiakoski et al. (2017, 2020).
description: {"references": ["Korkiakoski, M., Tuovinen, J. P., Aurela, M., Koskinen, M., Minkkinen, K., Ojanen, P., Penttil\u00e4, T., Rainne, J., Laurila, T., and Lohila, A.: Methane exchange at the peatland forest floor \u2013 automatic chamber system exposes the dynamics of small fluxes, Biogeosciences, 14(7), 1947\u20131967, https://doi.org/10.5194/bg-14-1947-2017, 2017", "Korkiakoski, M., Ojanen, P., Penttil\u00e4, T., Minkkinen, K., Sarkkola, S., Rainne, J., Laurila, T., and Lohila, A.: Impact of partial harvest on CH4 and N2O balances of a drained boreal peatland forest, Agric. For. Meteorol., 295, 108168, https://doi.org/10.1016/j.agrformet.2020.108168, 2020", "Koskinen, M., Minkkinen, K., Ojanen, P., K\u00e4m\u00e4r\u00e4inen, M., Laurila, T., and Lohila, A.: Measurements of CO2 exchange with an automated chamber system throughout the year: challenges in measuring night-time respiration on porous peat soil, Biogeosciences, 11(2), 347\u2013363, https://doi.org/10.5194/bg-11-347-2014, 2014"]}
DOI: 10.5281/zenodo.8214049
title: Ensemble Structure of the N-terminal Domain (1-267) of FUS in a Biomolecular Condensate

description: Primary double electron-electron resonance (DEER) EPR data, restraint files, and weighted ensembles for the N-terminal domain (1-267) of the protein fused in sarcoma (FUS) in the denatured state (3 M urea), the dispersed state, and the condensed state after liquid-liquid phase separation. The dispersed and condensed state ensembles are based on measurements on the same biphasic samples. An unrestrained ensemble with 5000 conformers based on residue-specific Ramachandran angle distributions is included as well. Modelling was performed with MMMx (https://github.com/gjeschke/MMMx).
description: SNSF grant CRSII5_170976
DOI: 10.5281/zenodo.8214048
title: Ensemble Structure of the N-terminal Domain (1-267) of FUS in a Biomolecular Condensate

description: Primary double electron-electron resonance (DEER) EPR data, restraint files, and weighted ensembles for the N-terminal domain (1-267) of the protein fused in sarcoma (FUS) in the denatured state (3 M urea), the dispersed state, and the condensed state after liquid-liquid phase separation. The dispersed and condensed state ensembles are based on measurements on the same biphasic samples. An unrestrained ensemble with 5000 conformers based on residue-specific Ramachandran angle distributions is included as well. Modelling was performed with MMMx (https://github.com/gjeschke/MMMx).
description: SNSF grant CRSII5_170976
DOI: 10.6084/m9.figshare.23632383
title: DATA AVAILABILITY: Eocene and Miocene crustal thermal state of the eastern Gangdese belt: Insights into the formation mechanisms of post-collisional Cu-fertile magmas

description: Zhou et al., 2023. Eocene and Miocene crustal thermal state of the eastern Gangdese belt: Insights into the formation mechanisms of post-collisional Cu-fertile magmas. <i>Mineralium Deposita</i>.<br>Table S1. Compilations of major and trace elements of Eocene and Miocene magmatic rocks from the Gangdese belt.Table S2. Compilations of major and trace elements of Paleocene-Eocene, Cretaceous, and Jurassic mafic rocks from the Gangdese belt.Table S3. Average composition of potential source mafic rocks for Eocene and Miocene intermediate to felsic magmatic rocks.Table S4. Mineral partition coefficients used in trace element modelling of partial melting of mafic rocks.Table S5. Thermodynamic and trace element modelling of partial melting of Eocene and Miocene source rocks.
DOI: 10.6084/m9.figshare.23632383.v1
title: DATA AVAILABILITY: Eocene and Miocene crustal thermal state of the eastern Gangdese belt: Insights into the formation mechanisms of post-collisional Cu-fertile magmas

description: Zhou et al., 2023. <strong>Eocene and Miocene crustal thermal state of the eastern Gangdese belt: Insights into the formation mechanisms of post-collisional Cu-fertile magmas.</strong> Data set S1. Compilations of major and trace elements of Eocene and Miocene magmatic rocks from the Gangdese belt. Data set S2. Compilations of major and trace elements of Paleocene-Eocene, Cretaceous, and Jurassic mafic rocks from the Gangdese belt. Data set S3. Thermodynamic and trace element modelling of partial melting of Eocene and Miocene source rocks
DOI: 10.5281/zenodo.7965848
title: Linguistic Matrix Theory Data

description: Dataset for analyzing the statistics of ensembles of matrices in the Linguistic Matrix Theory programme introduced in arxiv:1703.10252. A Python script (ratios.py) for testing the fit of the matrix data to the 13-parameter permutation invariant Gaussian matrix models introduced in arXiv:1809.07559 is given. Two Sage+Jupyter notebooks are provided that assist in the computation of theoretical higher-order moments based on permutation invariant matrix model along with a set of Python functions (MFunctions.py) that are useful for further Gaussianity tests as well as computational language tasks. The dataset was constructed by D. Kartsaklis for arXiv:1703.10252, ratios.py was developed for arXiv:1912.10839 and the Sage+Jupyter code was developed in arXiv:2104.03707. MFunctions.py was developed in arXiv:2202.06829.
DOI: 10.5281/zenodo.7965849
title: Linguistic Matrix Theory Data

description: Dataset for analyzing the statistics of ensembles of matrices in the Linguistic Matrix Theory programme introduced in arxiv:1703.10252. A Python script (ratios.py) for testing the fit of the matrix data to the 13-parameter permutation invariant Gaussian matrix models introduced in arXiv:1809.07559 is given. Two Sage+Jupyter notebooks are provided that assist in the computation of theoretical higher-order moments based on permutation invariant matrix model along with a set of Python functions (MFunctions.py) that are useful for further Gaussianity tests as well as computational language tasks. The dataset was constructed by D. Kartsaklis for arXiv:1703.10252, ratios.py was developed for arXiv:1912.10839 and the Sage+Jupyter code was developed in arXiv:2104.03707. MFunctions.py was developed in arXiv:2202.06829.
DOI: 10.5281/zenodo.8211274
title: Results of a Survey on Obstacle Resolving Model Data

description: This publication includes the results of the online survey "Obstacle resolving model data survey" which was available via https://www.uhh.de/orm-survey until June 2022. The data was collected between October 2019 and June 2022. The survey was created with the survey tool limesurvey (https://www.limesurvey.org/). The aim of the survey was to collect information on obstacle-resolving atmospheric models and their data, targeting the modeler community. As a part of the project AtMoDat (www.atmodat.de (https://www.atmodat.de/)) we invited the obstacle-resolving modeling community to contribute with their experience with obstacle-resolving models (ORM) and its model data, what models were used and how the user handles the data. We want to develop a data standard for obstacle-resolving model data, which shall be similar to existing standards. Therefore we need some information about the use and processing of model data. This survey asks about the usage of different obstacle-resolving models and/or outputs. The survey is divided into two parts, the first part contains questions about model properties and the second part contains questions about how the participants handle model data.
DOI: 10.5281/zenodo.8211273
title: Results of a Survey on Obstacle Resolving Model Data

description: This publication includes the results of the online survey "Obstacle resolving model data survey" which was available via https://www.uhh.de/orm-survey until June 2022. The data was collected between October 2019 and June 2022. The survey was created with the survey tool limesurvey (https://www.limesurvey.org/). The aim of the survey was to collect information on obstacle-resolving atmospheric models and their data, targeting the modeler community. As a part of the project AtMoDat (www.atmodat.de (https://www.atmodat.de/)) we invited the obstacle-resolving modeling community to contribute with their experience with obstacle-resolving models (ORM) and its model data, what models were used and how the user handles the data. We want to develop a data standard for obstacle-resolving model data, which shall be similar to existing standards. Therefore we need some information about the use and processing of model data. This survey asks about the usage of different obstacle-resolving models and/or outputs. The survey is divided into two parts, the first part contains questions about model properties and the second part contains questions about how the participants handle model data.