cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11276245
title: Process Behavior Corpus and Benchmarking Datasets

description: A corpus of process behaviors and benchmarking datasets for semantics-aware process mining tasks.

Files:



process_behavior_corpus.csv: the text corpus, which contains the behavior allowed by process models as sequences of activities (column string_traces).

T_SAD.csv: A benchmark dataset generated from the corpus to assess the following task: Given a trace σ, decide if σ is a valid execution of the underlying process or not, without knowing the behavior allowed in the process.Each row contains a trace (column trace) with a corresponding label (column anomalous) indicating whether the trace represents a valid execution of the underlying process. The set of activities that can occur in the process are also given (column unique_activities).

A_SAD.csv: A benchmark dataset generated from the corpus to assess the following task: Given an eventually-follows relation ef = a ≺ b ofa trace σ, decide if ef represents a valid execution order of the two activities a and b that are executed in a process or not, without knowing the behavior allowed in the process.Each row contains an eventually-follows relation (column eventually_follows) with a corresponding label (column out_of_order) indicating wether the two activities of the relation were executed in an invalid order (TRUE) or in a valid order (FALSE) according to the underlying process (model). The set of activities that can occur in the process are also given (column unique_activities).

S_NAP.csv: A benchmark dataset generated from the corpus to assess the following task: Given an event log L and a prefix p_k of length k, with 1 < k, predict the next activity a_k+1Each row contains a trace prefix (column prefix) with a corresponding next activity (column next) indicating the activity that should be performed next after the last activity of the prefix  according to the trace from which the prefix was generated. The set of activities that can occur in the process are also given (column unique_activities).

S-PMD.csv: A benchmark dataset generated from the corpus to assess the following tasks:



Given a set of possible activities (column unique_activities), generate a difectly follows graph (column dfg) that captures the trace semantics of the process model. 

Given a set of possible activities (column unique_activities), generate a simple process tree (column pt) that captures the trace semantics of the process model.




Reference and legal info:

The corpus and the benchmark datasets are generated using the SAP-SAM dataset:

Kampik, T., Warmuth, C., Sola, D., Schäfer, B., Axworthy, L., Ivarsson, E., Ouda, K., & Eickhoff, D. (2022). SAP Signavio Academic Models (0.5.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7012043The SAP-SAM dataset is published with a specific license (see "Rights"), which, therefore, also applies to the data published in this record.

THE DATASETS AND ASSOCIATED EVALUATION EXPERIMENTS ARE DESCRIBED IN THIS PAPER.

IN THIS REPOSITORY YOU FIND THE CODE AND RAW RESULTS OF EVALUATION EXPERIMENTS USING VARIOUS OPEN SOUCE LLMs TO SOLVE THE TASKS
DOI: 10.5281/zenodo.14273161
title: Process Behavior Corpus and Benchmarking Datasets

description: A corpus of process behaviors and benchmarking datasets for semantics-aware process mining tasks.

Files:



process_behavior_corpus.csv: the text corpus, which contains the behavior allowed by process models as sequences of activities (column string_traces).

T_SAD.csv: A benchmark dataset generated from the corpus to assess the following task: Given a trace σ, decide if σ is a valid execution of the underlying process or not, without knowing the behavior allowed in the process.Each row contains a trace (column trace) with a corresponding label (column anomalous) indicating whether the trace represents a valid execution of the underlying process. The set of activities that can occur in the process are also given (column unique_activities).

A_SAD.csv: A benchmark dataset generated from the corpus to assess the following task: Given an eventually-follows relation ef = a ≺ b ofa trace σ, decide if ef represents a valid execution order of the two activities a and b that are executed in a process or not, without knowing the behavior allowed in the process.Each row contains an eventually-follows relation (column eventually_follows) with a corresponding label (column out_of_order) indicating wether the two activities of the relation were executed in an invalid order (TRUE) or in a valid order (FALSE) according to the underlying process (model). The set of activities that can occur in the process are also given (column unique_activities).

S_NAP.csv: A benchmark dataset generated from the corpus to assess the following task: Given an event log L and a prefix p_k of length k, with 1 < k, predict the next activity a_k+1Each row contains a trace prefix (column prefix) with a corresponding next activity (column next) indicating the activity that should be performed next after the last activity of the prefix  according to the trace from which the prefix was generated. The set of activities that can occur in the process are also given (column unique_activities).

S-PMD.csv: A benchmark dataset generated from the corpus to assess the following tasks:



Given a set of possible activities (column unique_activities), generate a difectly follows graph (column dfg) that captures the trace semantics of the process model. 

Given a set of possible activities (column unique_activities), generate a simple process tree (column pt) that captures the trace semantics of the process model.




Reference and legal info:

The corpus and the benchmark datasets are generated using the SAP-SAM dataset:

Kampik, T., Warmuth, C., Sola, D., Schäfer, B., Axworthy, L., Ivarsson, E., Ouda, K., & Eickhoff, D. (2022). SAP Signavio Academic Models (0.5.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7012043The SAP-SAM dataset is published with a specific license (see "Rights"), which, therefore, also applies to the data published in this record.

THE DATASETS AND ASSOCIATED EVALUATION EXPERIMENTS ARE DESCRIBED IN THIS PAPER.

IN THIS REPOSITORY YOU FIND THE CODE AND RAW RESULTS OF EVALUATION EXPERIMENTS USING VARIOUS OPEN SOUCE LLMs TO SOLVE THE TASKS
DOI: 10.6084/m9.figshare.27611403.v1
title: Supplementary Material for: Treatment satisfaction in patients with hidradenitis suppurativa: A real-world survey from the EU5 and US

description: Introduction

Hidradenitis suppurativa (HS) is a debilitating, inflammatory skin disorder. Treatment strategies in patients with HS are challenging; real-world evidence in a HS population is warranted for greater disease understanding. The objective of this analysis was to describe real-world treatment patterns and treatment satisfaction in patients with HS. 

Methods

This was a cross-sectional market research survey with retrospective data collection in patients with HS from the United States and five European countries (France, Germany, Italy, Spain, United Kingdom) between November 2020 and April 2021, using physician- and patient-reported surveys. Eligible physicians were general dermatologists actively managing patients with HS; dermatologists were required to have consulted with ≥2 patients with HS in the previous 12 months. Adult (≥18 years) and adolescent (10‒17 years) HS patients visiting a participating dermatologist were included. Outcomes included treatment patterns, flare status, treatments prescribed in response to flares, previous surgeries, barriers to biologics, and patient- and physician-reported satisfaction with the disease control provided by treatment. 

Results

Survey data from 1787 patients were collected from 312 dermatologists. The most frequently prescribed treatments were topicals, oral antibiotics, and antiseptic washes/creams at diagnosis and sampling. At sampling, biologics were more frequently prescribed in patients with more severe disease (prescribed in 26.6%, 31.0% and 52.4% of patients with mild, moderate, and severe disease, respectively); oral antibiotics (48.8%), topicals (37.4%), and biologics (34.3%) were the most frequently prescribed treatment classes in response to a flare. Of patients currently not receiving a biologic, dermatologists reported that 18.9% of patients’ condition warranted their use. Approximately one quarter of dermatologists (24.5%) and patients (27.4%) were not satisfied with current treatment; of patients who were dissatisfied, 12.8% reported they would never raise their dissatisfaction with their doctor.

Conclusion 

These real-world data suggest a high disease burden and potential undertreatment in patients with HS. Patients received multiple treatments, and a notable proportion underwent surgery. Robustly integrating the patient voice in HS treatment decisions may lead to better outcomes and improved treatment satisfaction.
DOI: 10.5517/ccdc.csd.cc2kb277
title: CCDC 2363168: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb288
title: CCDC 2363169: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb299
title: CCDC 2363170: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2bb
title: CCDC 2363171: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2cc
title: CCDC 2363172: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2dd
title: CCDC 2363173: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2ff
title: CCDC 2363174: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2gg
title: CCDC 2363175: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2jj
title: CCDC 2363177: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2kk
title: CCDC 2363178: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2ll
title: CCDC 2363179: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2nn
title: CCDC 2363181: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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.cc2kb2hh
title: CCDC 2363176: Experimental Crystal Structure Determination

description: Related Article: Tao Jia, Ken-ichi Otake, Yifan Gu, Yuiga Nakamura, Yoshiki Kubota, Shogo Kawaguchi, Jiang Wu, Fengting Li, Susumu Kitagawa|2024|Cryst.Growth Des.|24|9425|doi:10.1021/acs.cgd.4c00868
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: 10.15468/dl.6syrh8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Julis poecilepterus Temminck & Schlegel, 1845",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016565-241126133413365/datasets/export for details.
DOI: 10.15482/usda.adc/1411953
title: Sustainable Corn CAP Research Data (USDA-NIFA Award No. 2011-68002-30190)

description: The Sustainable Corn CAP (Cropping Systems Coordinated Agricultural Project: Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems) was a multi-state transdisciplinary project supported by the USDA National Institute of Food and Agriculture (Award No. 2011-68002-30190). The field research experiments examined corn-based cropping systems in the U.S. Corn Belt region and potential farm-level adaptation practices to increase resilience to current and predicted impacts of climate change. Research data were collected from 2011 to 2015 at research sites in 8 states: Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. The research coverage area spanned 95.3°W to 81.9°W and 38.5°N to 44.7°N. Research sites encompassed a varying set of management practices including crop rotation, cover crop, tillage, drainage, and nitrogen management, with several sites having landscape position incorporated as an additional treatment. These treatments were typically arranged in a randomized complete block design as a complete factorial or main-split plot block with 3 to 4 replications per site. It should be noted that none of the sites were identical in terms of treatment structure and data collected as sites were a combination of previously and newly established experiments that aligned with project research goals. The dataset contain agronomic, soil, water, greenhouse gas, crop disease, and pest data collected from 30 sites. Standardized protocols were developed and followed by the project team for estimating C, N, and water footprints of corn production in the region and performing baseline monitoring. Variables measured during the five-year period include: grain and biomass yield, C and N content in crop grain and vegetation, soil water moisture and temperature, C and N concentration in soil, greenhouse gas fluxes, drainage water quality and quantity, groundwater table and others. Hourly or sub-hourly weather data are also provided for each location. In addition, the dataset includes site description (e.g. site location, plot area, soil type), field management information (e.g. planting, harvesting, tillage and fertilizer application dates, seeding rate, fertilizer and pesticide type and application rate), and experimental design (e.g. plot layouts, experimental treatments, variables measured). Users can query and download data from the Sustainable Corn CAP research web-accessible application. In addition to the data, the web tool also provides a list of over 100 referred journal articles as well as theses and dissertations related to the dataset. Other project-related materials such as fact sheets, videos, and extension publications are available for free download through the Iowa State University Extension and Outreach website.
DOI: 10.17632/pvn3xc3wy5.1
title: Electromagnetic Calorimeter Shower Images

description: Each HDF5 file has the following structure: `energy Dataset {100000, 1}` `layer_0 Dataset {100000, 3, 96}` `layer_1 Dataset {100000, 12, 12}` `layer_2 Dataset {100000, 12, 6}` `overflow Dataset {100000, 3}` In practice, each file is a collection of 100,000 calorimeter showers corresponding to the particle specified in the file name (eplus = positrons, gamma = photons, piplus = charged pions). The calorimeter we built is segmented longitudinally into three layer with different depths and granularities. In units of mm, the three layers have the following (eta, phi, z) dimensions: Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43) In the HDF5 files, the `energy` entry specifies the true energy of the incoming particle in units of GeV. `layer_0`, `layer_1`, and `layer_2` represents the energy deposited in each layer of the calorimeter in an image data format. Given the segmentation of each calorimeter layer, these images have dimensions 3x96 (in layer 0), 12x12 (in layer 1), and 12x6 (in layer 3). The `overflow` contains the amount of energy that was deposited outside of the calorimeter section we are considering.
DOI: 10.5281/zenodo.6366271
title: Fast Calorimeter Simulation Challenge 2022 - Dataset 2

description: This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels. dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation. More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/
DOI: 10.5281/zenodo.6366324
title: Fast Calorimeter Simulation Challenge 2022 - Dataset 3

description: This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the Par04 Geant4 example. dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation. More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at https://calochallenge.github.io/homepage/