cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7281/t1/om3omq/o34f5m
title: 1-02_Figure_3_4_5_10.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/a2mrwx
title: 2_Figure_6.zip

description: This zipped folder contains two python dictionary objects saved in Pickle format.  The file “Fyw_obs.pkl” contains a “LH” and TW” key with the modeled estimates of the mean annual young water fraction for each watershed (i.e., the closed circles in Figure 6).  Similarly, the file “Fyw_obs_novar.pkl” contains the estimates for the mean annual young water fraction assuming no within year variability (i.e., the open circles in Figure 6).
DOI: 10.7281/t1/om3omq/atnpcb
title: 3_Figure_7.zip

description: This zipped folder contains eight python dictionary objects saved in Pickle format. The files ending in “36s” have results using the optimal parameter set, while the files ending in “37s” have results assuming no inverse storage effect (i.e., lambda=0). The files beginning with “frac_temp” have 4 layers of keys.  The first key [“TW”,“LH”] indicates either Lower Hafren or Tanllwyth.  The second key [0,1,2,3,4,5] refers to five different rainfall scenarios.  Number zero is full rainfall variability.  Number one is number zero minus seasonality.  Number twp is number one minus within storm variability.  Number three is number two minus between storm variability.  Number four is no variability.  Number five is the historic period from 1999-2008. The third key [“yearly”, “summer”, “dec_mean”, “winter”, “winter_mean”, “jun_mean”, “summer_mean”, “mean”] indicates a time period of averaging. For example, “yearly” contains the mean age fraction of water per year, “dec_mean” contains the mean age fraction of water each year in December, and “mean” contains a single value for the entire period. The data record is 100 years long for rainfall scenarios 0-4, and 10 years long for rainfall scenario 5. The last key [1,2,3,…365] indicates the age fraction of water reported in days. For example, “90” return the fraction of water less than 90 days old in discharge. Thus, to summarize, the object frac_temp[‘TW’][1][‘summer’][90] returns an array with 100 values representing the yearly mean fraction of water younger than 90 days in the Tanllwyth watershed given a synthetically generated, 100 year precipitation input with full variability minus seasonality. The files beginning with “disch”, “prec”, and “stor” have the daily discharge [mm/day], precipitation [mm/day], and storage anomaly [mm] time-series for each watershed and for each of the five categories of rainfall. The time-series are 100 years long.
DOI: 10.7281/t1/om3omq/c1bjjp
title: 1-05_Figure_3_4_5_10_LH-1601-2400.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/tqvrxz
title: 4_Figure_8.zip

description: This zipped folder contains a python dictionary saved in Pickle format.  It contains the file “pattern_example_out_4_LH.pkl”, which is data used to plot Figure 8.   The first key is the number 0 thru 5 referring to panels a thru f of Figure 8.  The second key is the number 0-2 referring to the three subpanels of each panel from top to bottom.  The last set of keys are “P” (precipitation in mm/day), “Q” (discharge in mm/day), “Q_old” (discharge older than 90 days old in mm/day), “Q_young” (discharge younger than 90 days old in mm/day), “Fyw” (fraction of young water), “pws” (precipitation weighted storage or storage-based young water index in mm), and “t” (time in days).
DOI: 10.7281/t1/om3omq/vdune7
title: 1-06_Figure_3_4_5_10_LH-2401-3200.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/leeoo2
title: 5_Figure_9.zip

description: This zipped folder contains two python dictionaries saved in Pickle format.  The file “CC_01_PQ.pickle” contains keys for “LH” or “TW.  The second key is the scenario which is either “hist” (corresponding to the historic baseline), 2 (corresponding to the median change in ET), 50 (corresponding to the median change in ET and median change in rainfall, and 101 (corresponding to the median change in ET and extreme change in rainfall).  Each key contains an array with the backwards transit time distribution varying by age (366 rows representing a day each) and time (5845 columns representing a day each, from 1993 – 2008).  The CC_01_Q.pickle structure is similar except the final array is discharge over 5845 days in mm/day.
DOI: 10.7281/t1/om3omq/pssaea
title: 7_Rainfall_runoff_model.zip

description: These python scripts run the rainfall-runoff model described in the manuscript.  A brief description of each script is commented at the top of the code.  The main script is “def_rr_model_g_v12”.  It takes run dates, a rainfall time-series, a potential ET time-series, and parameters for the catchment sensitivity function as input.  It returns a time-series for discharge.  There are also various options for returning estimates of the simulated ET and storage anomaly.  In the process of generating the rainfall time-series, it uses the script “def_SQ_lookup.py” to convert discharge to the equivalent storage anomaly value.  Finally, the script “utils_v10.py” contains several short functions for calculating statistics and other tasks. The file “config_vars_v7a” contains some of the parameters used by the scripts.  These scripts are called by other scripts (not archived here) that produced the final modeled output data contained in the Figure_7.zip and the Figure_9.zip folders.
DOI: 10.7281/t1/om3omq/sizafo
title: 1-07_Figure_3_4_5_10_TW-0001-0800.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/trv2fr
title: 1-09_Figure_3_4_5_10_TW-1601-2400.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/x7bypd
title: 1-08_Figure_3_4_5_10_TW-0801-1600.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.7281/t1/om3omq/z6kgyz
title: 6_Rainfall_generator_model.zip

description: These python scripts run the rainfall generator described in the manuscript and supplemental materials.  A brief description of each script is commented at the top of the code.  The main script is “def_rain_gen_v4.py”.  It takes a start data, end date, and parameter set as input, and returns a daily rainfall time-series.  In the process of generating the rainfall time-series, it uses the script “def_within_storm_estimates.py”, which determines the distribution of rainfall within a single storm event.  The file “config_vars_v7a” contains some of the parameters used by the scripts.  These scripts are called by other scripts (not archived here) that produced the final modeled output data contained in the Figure_7.zip folder.
DOI: 10.7281/t1/om3omq/c6c7fw
title: 1-01_Figure_3_4_5_10_ReadMe.odt

description: Read me file for Figure_3_4_5_10.
DOI: 10.7281/t1/om3omq/pzqgb4
title: 1-10_Figure_3_4_5_10_TW-2401-3200.zip

description: See "1-01_Figure_3_4_5_10_ReadMe" for more details.
DOI: 10.25592/uhhfdm.12138
title: Account on The Sooninkee-Marabout Wars And The Role of Maba Jaahu And Seet Mati (NCAC_RDD_TAPE_0600A_B)

description: Sooninkee-Marabout wars Maba Jaahu Seet Mati Maba's ancestors back to Sunjata <sup><em>Alternative names: Jahou, Sait, Sayit, Sayid, Seed, Matty, Camara, Alhagi, Alhagie, Sidibeh, Bakari, Bakary, Bakery, Kebba</em></sup>
description: Published by NCAC - National Digital Archive of The Gambia, initiated by University of Hamburg, Asia-Africa-Institute, Germany, funded by Gerda Henkel Foundation, Germany
DOI: 10.5281/zenodo.5964240
title: SeaLiT Ontology - An extension of CIDOC-CRM for the modelling of Maritime History information

description: The <strong>SeaLiT Ontology</strong> is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to <strong>maritime history</strong>. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information. The ontology uses and extends the <strong>CIDOC Conceptual Reference Model</strong> (ISO 21127:2014), in particular version 7.1.1, as a general ontology of human activity, things and events happening in space and time. The ontology has been developed following a bottom-up process from primary data collected in the context of the <strong>SeaLiT Project</strong> (<em>Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s</em>). SeaLiT is an international research project, funded by the ERC Starting Grant 2016, which explores the transition from sail to steam navigation and its effects on seafaring populations in the Mediterranean and the Black Sea between the 1850s and the 1920s. More information about the considered data sources, as well as their transformation to a knowledge graph using the <strong>SeaLiT Ontology</strong>, can be found in the following paper: P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. https://doi.org/10.1145/3461460 [pdf, bib] The (resolvable) <strong>namespace </strong>of the ontology is: http://www.sealitproject.eu/ontology/ <strong>Knowledge graphs </strong>that make use of the SeaLiT Ontology are available at: https://zenodo.org/record/6460841. These RDF datasets integrate information of 16 different types of archival sources related to maritime history, including crew lists, payrolls, registers of different types, censuses, and employment records.
DOI: 10.5281/zenodo.6797750
title: SeaLiT Ontology - An extension of CIDOC-CRM for the modelling of Maritime History information

description: The <strong>SeaLiT Ontology</strong> is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to <strong>maritime history</strong>. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information. The ontology uses and extends the <strong>CIDOC Conceptual Reference Model</strong> (ISO 21127:2014), in particular version 7.1.1, as a general ontology of human activity, things and events happening in space and time. The ontology has been developed following a bottom-up process from primary data collected in the context of the <strong>SeaLiT Project</strong> (<em>Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s</em>). SeaLiT is an international research project, funded by the ERC Starting Grant 2016, which explores the transition from sail to steam navigation and its effects on seafaring populations in the Mediterranean and the Black Sea between the 1850s and the 1920s. More information about the considered data sources, as well as their transformation to a knowledge graph using the <strong>SeaLiT Ontology</strong>, can be found in the following paper: P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. https://doi.org/10.1145/3461460 [pdf, bib] The (resolvable) <strong>namespace </strong>of the ontology is: http://www.sealitproject.eu/ontology/ An <strong>OWL implementation</strong> of the ontology is available at: https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.1.owl <strong>Knowledge graphs </strong>that make use of the SeaLiT Ontology are available at: https://zenodo.org/record/6460841. These RDF datasets integrate information of 16 different types of archival sources related to maritime history, including crew lists, payrolls, registers of different types, censuses, and employment records.
DOI: 10.5281/zenodo.7565158
title: ConfoMap - Tool function experiment

description: The dataset results from an experiment called "tool function experiment" conducted at TraCEr Laboratory for Traceology and Controlled Experiments. The goal of this sequential experiment was to test tool performance of Late Middle Palaeolithic <em>Keilmesser</em> by examining two relevant aspects concerning their edge design: the raw material and the edge angle. To do so, a controlled, mechanical setup was used. The samples and the contact material were standardised to limit the number of confounding factors. As tasks, unidirectional cutting and carving movements have been performed. One element of assessing the tool performance of each experimental standard sample was the penetration depth measured on the contact material. Thus, after the experiment, the contact material was documented with a Sensofar S wide (Sensofar Metrology, Spain), a 3D optical metrology system. Based on the data acquired with the S wide and processed in ConfoMap (a derivative of MountainsMap Imaging Topography), the depth and the width of the cuts and scratches were quantified (https://doi.org/10.5281/zenodo.7564605). In total, two templates have been used in ConfoMap: 1) "Patch surfaces of the bone plates acquired with the Sensofar S-wide" An initial template was needed to patch the single quadrants together, so that the grooves are complete again. This involves levelling and aligning. Afterwards, each groove can be exported individually. 2) "Processing on single grooves" This template extracts the topography layer of the grooves and calculates a mean profile of a series of 30 profiles. After levelling the mean profiles, the height and the width of each groove can be calculated. Details about the experiment and the data acquisition with the Sensofar S-wide can also be found on
DOI: 10.5281/zenodo.7565159
title: ConfoMap - Tool function experiment

description: The dataset results from an experiment called "tool function experiment" conducted at TraCEr Laboratory for Traceology and Controlled Experiments. The goal of this sequential experiment was to test tool performance of Late Middle Palaeolithic <em>Keilmesser</em> by examining two relevant aspects concerning their edge design: the raw material and the edge angle. To do so, a controlled, mechanical setup was used. The samples and the contact material were standardised to limit the number of confounding factors. As tasks, unidirectional cutting and carving movements have been performed. One element of assessing the tool performance of each experimental standard sample was the penetration depth measured on the contact material. Thus, after the experiment, the contact material was documented with a Sensofar S wide (Sensofar Metrology, Spain), a 3D optical metrology system. Based on the data acquired with the S wide and processed in ConfoMap (a derivative of MountainsMap Imaging Topography), the depth and the width of the cuts and scratches were quantified (https://doi.org/10.5281/zenodo.7564605). In total, two templates have been used in ConfoMap: 1) "Patch surfaces of the bone plates acquired with the Sensofar S-wide" An initial template was needed to patch the single quadrants together, so that the grooves are complete again. This involves levelling and aligning. Afterwards, each groove can be exported individually. 2) "Processing on single grooves" This template extracts the topography layer of the grooves and calculates a mean profile of a series of 30 profiles. After levelling the mean profiles, the height and the width of each groove can be calculated. Details about the experiment and the data acquisition with the Sensofar S-wide can also be found on
DOI: 10.5281/zenodo.7979319
title: Data for Demonstration of quantum-digital payments

description: Timetags that lead to coincidence count events in a 3ns window for Client and TTP.<br> For linear (commitment to M0) and diagonal (M1) basis. Format:<br> &lt;uint8 c&gt;\t&lt;double t&gt; c: channel on timetagger<br> t: photon arrival time in ns <br> TTP Channels:<br> 2: H<br> 3: V<br> 4: +<br> 5: - Client Channels:<br> 2: H / +<br> 4: V / -
DOI: 10.18712/nsd-nsd2066-v3
title: Investment in Renewable Electricity under Climate Policy Uncertainty, 2010.

description: In the coming years, the world will need more electric power from renewable resources , and Norway is well positioned to contribute with additional renewable electricity capacity. Decisions on whether and when to invest in new capacity will depend on uncertainty with respect to future cash flows generated by renewable electricity projects. One source of uncertainty is climate policy; either with respect to the future climate policy itself or with respect to the impact of existing climate policies on future cash flows. Real option theory can be used to translate climate policy uncertainty into investment risk. A license to build a power plant is a real option, where the investor has the right, but not the obligation, to pay the investment cost to get the cash flow of the project. Faced with a risky irreversible decision, investors will val ue the opportunity to gain additional information about likely future conditions affecting the project. This could mean delaying investment until uncertainty has been partly resolved. In this project the validity of Real option theory is tested in predicting how investors respond to climate policy uncertainty. The starting point is two data sets on production licenses; one on small hydro power projects and one on land-based windmill projects. Whether actual investment behavior is consistent with the predictions by Real option models was tested via quantitative models. These studies were complemented with surveys and interviews with investors, and dialog with a stakeholder reference group. Based on research results the project presents extensions of Real option theory as well as suggest how a careful design of new policy instruments may reduce investors risk and thus the social cost of achieving climate policy targets.
DOI: 10.7281/t1/sq6fo9/65ofj9
title: basin_trim_abay_landcover.zip

description: Band sequential binary (BSQ) raster and associated header file (HDR) for the land cover classification map presented in Eggen et al. (2016).
DOI: 10.7281/t1/sq6fo9/yw3nof
title: ReadMe_basin_trim_abay_landcover.txt

description: :unav
DOI: 10.25592/uhhfdm.12208
title: An Interview About Reverend Fayi's Life History With Reference to Slave Trade And Colonialism (NCAC_RDD_TAPE_0706A)

description: Continuation of TAPE_0705 Liberated slaves The Sierra Leone families "West Indians" Bathurst trading firms and their agents Labourer ("Gurmetta") <em><sup>Alternative names: Faye, Sidibeh, Bakary, Bakari, Bakery, Kebba, Conteh, Konteh, Isatou, Jobarteh, Jobateh</sup></em>
description: Published by NCAC - National Digital Archive of The Gambia, initiated by University of Hamburg, Asia-Africa-Institute, Germany, funded by Gerda Henkel Foundation, Germany
DOI: 10.5281/zenodo.7750142
title: Evaluation-of-templates-for-requirements-documentation

description: This repository contains data and code related to experimentation on a comparative evaluation of different template notations for requirements documentation in semi-formal natural language.
description: Pre-final version for (double blind) peer-review. Final version linked to gitHub moved to https://doi.org/10.5281/zenodo.8020672
DOI: 10.5281/zenodo.6321277
title: Evaluation-of-templates-for-requirements-documentation

description: This repository contains data and code related to experimentation on a comparative evaluation of different template notations for requirements documentation in semi-formal natural language.
description: Pre-final version for (double blind) peer-review. Final version linked to gitHub moved to https://doi.org/10.5281/zenodo.8020672
DOI: 10.5281/zenodo.8020390
title: Dog Health Vitals Dataset

description: The Dog Health Vitals Dataset is a collection of recordings and vital statistics related to the health of dogs. It includes data captured during various recording sessions, providing insights into the physiological characteristics of the dogs.
description: {"references": ["Jarkoff, H., Lorre, G., & Humbert, E. (2023). Assessing the Accuracy of a Smart Collar for Dogs: Predictive Performance for Heart and Breathing Rates on a Large Scale Dataset. Preprint available on bioRxiv."]}
DOI: 10.5281/zenodo.8020389
title: Dog Health Vitals Dataset

description: The Dog Health Vitals Dataset is a collection of recordings and vital statistics related to the health of dogs. It includes data captured during various recording sessions, providing insights into the physiological characteristics of the dogs.
description: {"references": ["Jarkoff, H., Lorre, G., & Humbert, E. (2023). Assessing the Accuracy of a Smart Collar for Dogs: Predictive Performance for Heart and Breathing Rates on a Large Scale Dataset. Preprint available on bioRxiv."]}
DOI: 10.5517/ccdc.csd.cc2bmdn1
title: CCDC 2163603: Experimental Crystal Structure Determination

description: Related Article: Mohammad Taha, Edit Y. Tshuva|2023|Dalton Trans.|52|7664|doi:10.1039/D3DT00984J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ckh1h
title: CCDC 2191546: Experimental Crystal Structure Determination

description: Related Article: Lorenzo Marchi, Simone Fantuzzi, Andrea Cingolani, Alessandro Messori, Rita Mazzoni, Stefano Zacchini, Marina Cocchi, Luca Rigamonti|2023|Dalton Trans.|52|7684|doi:10.1039/D3DT00847A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2flmsg
title: CCDC 2252236: Experimental Crystal Structure Determination

description: Related Article: Tao Zhang, Yifeng Ni, Jiangtao Sun|2023|Org.Biomol.Chem.|21|4583|doi:10.1039/D3OB00569K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/7sxh-vw94
title: Mutimodal Mutual Attention Network

description: — Medical image segmentation is a crucial aspect of medical image processing, and has been widely used in the detection and clinical diagnosis for brain, lung, liver, heart and other diseases. In this paper, we propose a novel multimodal mutual attention network, called MMAUNet, for medical image segmentation. MMA-UNet is divided into two parts. The first part obtains more highdimensional features by skip connection and improved network structure. The second part incorporates a multimodal mutual attention mechanism, encompassing feature mutual attention, spatial mutual attention, and channel mutual attention. This mechanism facilitates the effective fusion of high-dimensional and low-dimensional features, leading to enhanced context information. Experimental results on Kagglelung dataset, Liver dataset, Cell dataset, Drive dataset and Kvasir-SEG dataset show that MMA-UNet has achieved better segmentation performance than that of other baseline methods, on lung, liver, cell contour, retinal vessel and polyps, etc.
DOI: 10.34740/kaggle/dsv/5884293
title: 2021 World Population (updated daily)