You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Specifications table {#s0005}
====================

TableSubject area*Structural Biology*More specific subject area*Cryo-EM Models*Type of data*Figures*How data was acquired*Computational analysis*Data format*Analyzed*Experimental factors*None*Experimental features*None*Data source location*Rutgers University*Data accessibility<http://model-compare.emdatabank.org/data/scores><http://model-compare.emdatabank.org/em_score_boxplots.cgi>

Value of the data {#s0010}
=================

•The data reveal ranges of evaluation scores for models submitted to cryo-EM model challenge and provide descriptive statistics.•The data show differences in accuracy of cryo-EM models generated with different modeling techniques.•The data can serve as a benchmark for future cryo-EM modeling challenges.•The data can be compared with the data in other structure modeling experiments (e.g., CASP [@bib1]).

1. Data {#s0015}
=======

142 protein structure models were submitted on eight modeling targets of the second Cryo-EM model challenge (2015--2016) [@bib2]. A computational system was developed to estimate the accuracy of the models [@bib3]. Each model was evaluated using a suite of 15 software packages and, as a result, 54 accuracy scores were generated per model. All scores are presented in tables and graphs of the dedicated web infrastructure (<http://model-compare.emdatabank.org>). Some scores were analyzed in the accompanying paper [@bib3]. This article provides results of the descriptive statistical analysis of the complete set of evaluation scores.

2. Materials and methods {#s0020}
========================

2.1. Model types {#s0025}
----------------

Each model submitted to the second cryo-EM model challenge was accompanied by basic information about the modeling technique used. This information indicated whether model was built *ab initio* by fitting coordinates to density maps or by optimizing already available related structures. Based on this information, models were binned into two categories: *ab initio* or optimized. [Fig. 1](#f0005){ref-type="fig"}, [Fig. 2](#f0010){ref-type="fig"}, [Fig. 3](#f0015){ref-type="fig"} in this paper show distributions of the evaluation scores separately for *ab initio* and optimized models, while [Figs. 4](#f0020){ref-type="fig"} and [5](#f0025){ref-type="fig"} show the distributions for all models in one dataset (see description of the data presented in the figures in [Section 4.4](#s0040){ref-type="sec"} below).Fig. 1Distribution of the evaluation scores calculated exclusively from the model coordinates for different types of models. For each measure (specified in the *x*-axis title), a blue boxplot shows the score distribution for models built starting from reference structure, while a red boxplot -- for models built ab initio. Panel (A) shows evaluation scores for models' subunits (monomeric evaluation mode), while panel (B) for whole multimeric models.Fig. 1Fig. 2Distribution of the evaluation scores calculated by comparing models with reference structures. For each measure (specified in the *x*-axis title), a blue boxplot shows the score distribution for models built starting from reference structure, while a red boxplot -- for models built *ab initio*. Panel (A) shows evaluation scores for models' subunits (monomeric evaluation mode), while panel (B) for whole multimeric models.Fig. 2Fig. 3Distribution of the evaluation scores estimating fit of models to density maps (panel A) and similarity of models to other submitted models (panel B). For each measure (specified in the *x*-axis title), a blue boxplot shows the score distribution for models built starting from reference structure, while a red boxplot -- for models built *ab initio*.Fig. 3Fig. 4Distribution of the evaluation scores shown in Fig. 8 of Ref. [@bib3] for all submitted models (optimization and *ab initio* models pulled together). Left set of boxplots shows scores from multimeric evaluations, right set -- from monomeric ones.Fig. 4Fig. 5Distribution of the evaluation scores not included in [Fig. 4](#f0020){ref-type="fig"} for all submitted models.Fig. 5

2.2. Box plots {#s0030}
--------------

Data in the [Fig. 1](#f0005){ref-type="fig"}, [Fig. 2](#f0010){ref-type="fig"}, [Fig. 3](#f0015){ref-type="fig"}, [Fig. 4](#f0020){ref-type="fig"}, [Fig. 5](#f0025){ref-type="fig"} are presented as box plots. Box boundaries correspond to the *Q*~1~ = 25th (bottom) and *Q*~3~ = 75th (top) percentiles in the data; the horizontal line inside the box corresponds to the median (*Q*~2~). The height of the box defines the interquartile range (IQR = *Q*~3~-*Q*~1~). The height of the whiskers shows the range of the values outside the interquartile range, but within 1.5 IQR. The dots correspond to outliers, i.e. values outside the 1.5 IQR range.

2.3. Evaluation tracks and packages {#s0035}
-----------------------------------

Submitted models were evaluated in four evaluation tracks:(1)directly from the model coordinates, i.e. without referring to density maps or other available structures (software packages used: *MolProbity 4.4* [@bib4]*, phenix.model_vs_map* module (from *PHENIX 1.11.1-2575 distribution* [@bib5])*, DFIRE* [@bib6]*, ProQ3* [@bib7]*, QMEAN* [@bib8]);(2)comparing the model coordinates to those of reference structures (software packages used: *LGA* [@bib9]*, TM* [@bib10], [@bib11]*, LDDT* [@bib12]*, CAD* [@bib13]*, QS-score* [@bib14]*, IFaceCheck* [@bib15]*, phenix.chain_comparison* module [@bib5]);(3)checking fit of the model coordinates to the experimental 3DEM density maps (software packages used: *TEMPy 1.0* [@bib16], [@bib17]*, EMRinger* [@bib18]*, phenix.model_vs_map* module [@bib5]);(4)comparing coordinates of each model to those of other submitted models (software packages used: *Davis-QAconsensus* [@bib19]).

2.4. Score distributions {#s0040}
------------------------

Detailed explanation of the evaluation scores is provided in the accompanying paper [@bib3].

[Fig. 1](#f0005){ref-type="fig"} illustrates distributions of evaluation scores calculated directly from model coordinates (evaluation track 2.3.1). Panel (A) shows scores calculated on representative model subunits, while panel (B) -- on whole multimeric structures. The figure includes box plots for the following scores:

Panel (A):•Molprb(mon) -- MolProbity׳s combined MPscore;•Molprb(mon):rot_out -- MolProbity׳s rotamer outlier score;•Molprb(mon):clash -- MolProbity׳s clash score;•Molprb(mon):ram_fv -- MolProbity׳s Ramachandran favored score;•Molprb(mon):ram_out -- MolProbity׳s Ramachandran outlier score;•Log(-DFIRE) -- logarithm of negative DFIRE energy score;•ProQ3 -- score from the ProQ3 program ran with default parameters;•QMEAN -- score from the QMEAN program ran with default parameters.

Panel (B):

•\[Molprb(mult):ram_fv /Molprb(mult):clash /Molprb(mult):ram_out /Molprb(mult):rot_out\] -- MolProbity׳s \[Ramachandran favored score /clash score /Ramachandran outlier score /rotamer outlier\] score;•\[PHENIX:bond_rmsd /PHENIX:angle_rmsd /PHENIX:planar_rmsd /PHENIX:chiral_rmsd /PHENIX:dihedr_rmsd\] -- the RMSD on \[bond /angle /planarity /chirality /dihedral angle\] deviations calculated with the *phenix.model_vs_map* module;•PHENIX:bond_max -- the maximum deviation of bond distances from ideal values (in Å);•PHENIX:angle_max -- the maximum deviation of angles from ideal values (in deg.);•PHENIX:planar_max -- the maximum deviation of peptide bond planarity from ideal values (in deg.);•PHENIX:chiral_max -- the maximum deviation of chirality score from ideal values;•PHENIX:dihedr_max -- the maximum deviation of dihedral angles from ideal values (in deg.).

[Fig. 2](#f0010){ref-type="fig"} illustrates distributions of evaluation scores calculated by comparing models with reference structures (evaluation track 2.3.2). Panel (A) shows scores calculated on representative model subunits, while panel (B) -- on whole multimeric structures. The figure includes box plots for the following scores:

Panel (A):

•GDT_TS, GDT_HA -- scores from the LGA package ran with parameters: '-3 -sda -d:4');•LGA_S -- score from the LGA package ran with parameters: '-4 -sia -d:4');•RMSD -- root mean square deviation on Cα atoms of the representative chain (as reported by the LGA package);•TMscore, TMalign -- scores from the TM package ran with default parameters;•CAD -- CAD_aa variant of the CAD score calculated on all atoms;•LDDT -- a score from the LDDT package run with 15 Å inclusion radius.

Panel (B):

•\[QS-global /QS-best\] -- Quaternary Structure score calculated on \[all interfaces /best interface\] with the *QS-score* package;•\[QS:LDDT /QS:RMSD\] -- \[LDDT and /Cα RMSD\] scores calculated on all chains by the *QS-score* package;•IFaceCheck:F1_max -- maximum *F1* statistics from among those calculated on all interfaces calculated with the *IFaceCheck* package;•IFaceCheck:Jd_min -- the minimum *Jaccard distance* from among those calculated on all interfaces;•IFaceCheck:prec_max -- maximum *precision* from among those calculated on all interfaces;•IFaceCheck:recall_max -- maximum *recall* from among those calculated on all interfaces;•IFaceCheck:RMSD_min -- minimum RMSD on target interface atoms from among those calculated on all interfaces;•\[IFaceCheck:F1_avg /IFaceCheck:Jd_avg /IFaceCheck:prec_avg /IFaceCheck:recall_avg /IFaceCheck:RMSD_avg\] -- the \[F1 /Jaccard distance /precision /recall /interface RMSD\] scores averaged over all interfaces;•\[PHENIX:CA-score /PHENIX:seq_match\] --scores generated with the *phenix.chain_comparison* module.

[Fig. 3](#f0015){ref-type="fig"} illustrates distributions of evaluation scores estimating fit of models to density maps (panel A, evaluation track 2.3.3) and similarity of models to other submitted models (panel B, evaluation track 2.3.4). The figure includes box plots for the following scores:

Panel (A):

•\[PHENIX:overall_FSC /PHENIX:boxCC\] -- the \[overall Fourier Shell Correlation in reciprocal Fourier space /per-chain box cross-correlation\] calculated with the *phenix.model_vs_map* module;•\[TEMPy:CCC /TEMPy:LAP /TEMPy:ENV /TEMPy:MI\] -- TEMPY׳s \[cross-correlation coefficient /Laplacian-filtered cross-correlation /envelope /mutual information\] scores;•EMRinger -- EMRinger score calculated using the *phenix:emringer* module.Panel (B):•Davis-QA -- a model consensus score calculated by averaging the GDT_TS scores from pairwise comparisons of the model to all others.

[Figs. 4](#f0020){ref-type="fig"} and [5](#f0025){ref-type="fig"} illustrate distributions of evaluation scores presented in [Fig. 1](#f0005){ref-type="fig"}, [Fig. 2](#f0010){ref-type="fig"}, [Fig. 3](#f0015){ref-type="fig"} when all models (optimization and *ab initio*) are grouped in one dataset. Score names are as described above for [Fig. 1](#f0005){ref-type="fig"}, [Fig. 2](#f0010){ref-type="fig"}, [Fig. 3](#f0015){ref-type="fig"}.

Transparency document. Supplementary material {#s0050}
=============================================

Supplementary material

Authors acknowledge support of the National Institute of General Medical Sciences, USA: Grant R01GM079429 to WC, and Grant P01GM063210 to PDA.

Transparency data associated with this article can be found in the online version at [10.1016/j.dib.2018.08.214](http://dx.doi.org/10.1016/j.dib.2018.08.214){#ir0008}.